---
title: "Retrieval-Augmented Generation for Natural Language"
description: "This survey describes RAG for NLP as a system with three parts. 276 references, cited by 17 papers here."
authors: ["Shangyu Wu", "Ying Xiong", "Yufei Cui", "Haolun Wu", "Can Chen", "Ye Yuan", "Lianming Huang", "Xue Liu", "Tei-Wei Kuo", "Nan Guan", "Chun Jason Xue"]
year: 2024
venue: "arXiv:2407.13193"
source: "https://arxiv.org/abs/2407.13193"
doi: "10.48550/arxiv.2407.13193"
arxiv: "2407.13193"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wu-2024-retrieval-augmented-generation-natural-language/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wu-2024-retrieval-augmented-generation-natural-language/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2024](https://md.research.elamur.ai/free/en/papers/2024/page/4/) / Retrieval-Augmented Generation for Natural Language Processing: A Survey

# Retrieval-Augmented Generation for Natural Language Processing: A Survey

**Authors:** Shangyu Wu, [Ying Xiong](https://md.research.elamur.ai/free/en/authors/ying-xiong/), [Yufei Cui](https://md.research.elamur.ai/free/en/authors/yufei-cui/), [Haolun Wu](https://md.research.elamur.ai/free/en/authors/haolun-wu/), [Can Chen](https://md.research.elamur.ai/free/en/authors/can-chen/), [Ye Yuan](https://md.research.elamur.ai/free/en/authors/ye-yuan/), Lianming Huang, [Xue Liu](https://md.research.elamur.ai/free/en/authors/xue-liu/), Tei-Wei Kuo, Nan Guan and Chun Jason Xue · **Year:** 2024 · **Venue:** [arXiv:2407.13193](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/) ([Artificial Intelligence Review 2024](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/2024/)) · **Publisher:** [Springer Science+Business Media](https://md.research.elamur.ai/free/en/publishers/springer-science-business-media/) · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2407.13193) · [PDF](https://arxiv.org/pdf/2407.13193) · [DOI 10.48550/arxiv.2407.13193](https://doi.org/10.48550/arxiv.2407.13193) · [arXiv 2407.13193](https://arxiv.org/abs/2407.13193) · [OpenAlex](https://openalex.org/W4402854593)

**Citations:** 69 citations (OpenAlex, 2026-10-14) · [17](https://md.research.elamur.ai/free/en/papers/wu-2024-retrieval-augmented-generation-natural-language/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Large language models store knowledge implicitly in their weights. As a result, they can produce fluent but false answers, they need costly retraining to refresh facts, and they often lack depth in specialised domains. Retrieval-augmented generation (RAG) addresses these issues by consulting an external knowledge base at inference time. This survey describes RAG for NLP as a system with three parts. The first is a retriever, covering chunking, encoding, index and vector database construction, ANN search and post-processing. The second is retrieval fusion, and the third is a generator. The core contribution is a four-way taxonomy of how retrieved material enters the model: query-based, logits-based, latent and parametric fusion. The authors compare these by accessibility, efficiency, implementation effort and use cases, and discuss hybrid designs. They also review RAG across NLP tasks, evaluation methods and benchmark weaknesses, training with and without knowledge-base updates, deployment tooling, and open problems such as security, efficiency and GraphRAG. Being a survey, it reports no new experiments. Its value is a structured map of the field plus runnable tutorial code.

## Key points
- The fusion taxonomy has four types:
  - **Query-based:** retrieved chunks are concatenated with the input.
  - **Logits-based:** output logits are ensembled or calibrated.
  - **Latent:** retrieval representations are injected into the generator's hidden states.
  - **Parametric:** the top-k relevant LoRA modules are retrieved and merged into the weights.
- Generators fall into three groups:
  - proprietary models (GPT, Gemini);
  - open-weight models (Mistral, Qwen, DeepSeek, Llama);
  - retrieval-aware architectures (RETRO, Enc-Dec).
- The survey is positioned against six earlier RAG surveys. According to the authors, it is the only one that ships tutorial code: https://github.com/luffy06/RAG-Tutorials.
- Covered tasks include language modeling, machine translation, summarization, question answering, information extraction, text classification and dialogue systems.
- GraphRAG (Edge et al. 2024; open-source implementation by Microsoft) builds an entity graph with community summaries. The cited analyses (Xiang et al. 2025) show it does not consistently beat plain RAG and carries higher latency and indexing cost.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [McGill University](https://md.research.elamur.ai/free/en/universities/mcgill-university/) — University, Montreal, Canada · 87 works · 11,027 citations
- [Mohamed bin Zayed University of Artificial Intelligence](https://md.research.elamur.ai/free/en/universities/mohamed-bin-zayed-university-of-artificial-intelligence/) — University, Abu Dhabi, United Arab Emirates · 124 works · 5,676 citations
- [National Taiwan University](https://md.research.elamur.ai/free/en/universities/national-taiwan-university/) — University, Taipei, Taiwan · 29 works · 2,116 citations
- [City University of Hong Kong](https://md.research.elamur.ai/free/en/universities/city-university-of-hong-kong/) — University, Hong Kong, China · 45 works · 3,014 citations

📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 2,869 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,437 works

🧭 Topics (1): [🔍 Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · 473 works

## Cite

Cite as: Shangyu Wu, Ying Xiong, Yufei Cui, Haolun Wu, Can Chen, Ye Yuan, Lianming Huang, Xue Liu, Tei-Wei Kuo, Nan Guan and Chun Jason Xue. 2024. Retrieval-Augmented Generation for Natural Language Processing: A Survey. arXiv:2407.13193.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{wu2024retrieval,
  title = {{Retrieval-Augmented Generation for Natural Language Processing: A Survey}},
  author = {Shangyu Wu, Ying Xiong, Yufei Cui, Haolun Wu, Can Chen, Ye Yuan, Lianming Huang, Xue Liu, Tei-Wei Kuo, Nan Guan and Chun Jason Xue},
  year = {2024},
  doi = {10.48550/arxiv.2407.13193},
  eprint = {2407.13193},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2407.13193}
}
```

- [← previous MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning](https://md.research.elamur.ai/free/en/papers/liu-2024-mmc-advancing-multimodal-chart-understanding/)
- [next → Evaluating Mathematical Reasoning Beyond Accuracy](https://md.research.elamur.ai/free/en/papers/xia-2024-evaluating-mathematical-reasoning-accuracy/)
- [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/4/)

## References

276 references, 88 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. (2025a) Llm01:2025 prompt injection - owasp gen ai security project. URL [https://genai.owasp.org/llmrisk/llm01-prompt-injection/](https://genai.owasp.org/llmrisk/llm01-prompt-injection/) (2025b) Llm08:2025 vector and embedding weaknesses -owasp gen ai security project. URL https://genai.owasp.org/llmrisk/ llm082025-vector-and-embedding-weaknesses/ Abdalla… — [arXiv:2601.09523](https://arxiv.org/abs/2601.09523) [↗](https://genai.owasp.org/llmrisk/llm01-prompt-injection/)
2. Abnar S, Dehghani M, Neyshabur B, et al (2022) Exploring the limits of large scale pre-training. In: Proceedings of The Tenth International Conference on Learning Representations (ICLR) [find](https://scholar.google.com/scholar?q=Abnar+S%2C+Dehghani+M%2C+Neyshabur+B%2C+et+al+%282022%29+Exploring+the+limits+of+large+scale+pre%2Dtraining%2E+In%3A+Proceedings+of+The+Tenth+International+Conference+on+Learning+Representations+%28ICLR%29)
3. Ainslie J, Lee-Thorp J, de Jong M, et al (2023) [GQA: training generalized multi-query transformer models from multi-head checkpoints](https://md.research.elamur.ai/free/en/papers/ainslie-2023-gqa-training-generalized-multi-query/). In: Bouamor H, Pino J, Bali K (eds) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 4895–4901
4. AlKhamissi B, Li M, Celikyilmaz A, et al (2022) A review on language models as knowledge bases. CoRR abs/2204.06031 [find](https://scholar.google.com/scholar?q=AlKhamissi+B%2C+Li+M%2C+Celikyilmaz+A%2C+et+al+%282022%29+A+review+on+language+models+as+knowledge+bases%2E+CoRR+abs%2F2204%2E06031)
5. Amazon Web Services (2026) Retrieving information from data sources using amazon bedrock knowledge bases. [https://docs.aws.amazon.com/bedrock/latest/userguide/](https://docs.aws.amazon.com/bedrock/latest/userguide/) kb-how-retrieval.html, accessed: 2026-03-04 [↗](https://docs.aws.amazon.com/bedrock/latest/userguide/)
6. Anil R, Borgeaud S, Wu Y, et al (2023) [Gemini: A family of highly capable multimodal models](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/). CoRR abs/2312.11805
7. Arefeen MA, Debnath B, Chakradhar S (2024) Leancontext: Cost-efficient domain-specific question answering using llms. Natural Language Processing Journal 7:100065. https://doi.org/10.1016/J.NLP.2024.100065 — [doi:10.1016/j.nlp.2024.100065](https://doi.org/10.1016/j.nlp.2024.100065) [↗](https://doi.org/10.1016/J.NLP.2024.100065)
8. Asai A, Wu Z, Wang Y, et al (2024) [Self-rag: Learning to retrieve, generate, and critique through self-reflection](https://md.research.elamur.ai/free/en/papers/asai-2023-self-rag-learning-retrieve-generate/). In: Proceedings of The Twelfth International Conference on Learning Representations (ICLR)
9. Baek J, Aji AF, Saffari A (2023) [Knowledge-augmented language model prompting for zero-shot knowledge graph question answering](https://md.research.elamur.ai/free/en/papers/baek-2023-knowledge-augmented-language-model-prompting/). CoRR abs/2306.04136
10. Bai H, Zhang W, Hou L, et al (2021) [Binarybert: Pushing the limit of BERT quantization](https://md.research.elamur.ai/free/en/papers/bai-2021-binarybert-pushing-limit-bert-quantization/). In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL/IJCNLP), pp 4334–4348
11. Bertsch A, Alon U, Neubig G, et al (2023) Unlimiformer: Long-range transformers with unlimited length input. In: Advances in Neural Information Processing Systems 36 (NeurIPS) [find](https://scholar.google.com/scholar?q=Bertsch+A%2C+Alon+U%2C+Neubig+G%2C+et+al+%282023%29+Unlimiformer%3A+Long%2Drange+transformers+with+unlimited+length+input%2E+In%3A+Advances+in+Neural+Information+Processing+Systems+36+%28NeurIPS%29)
12. Borgeaud S, Mensch A, Hoffmann J, et al (2022) Improving language models by retrieving from trillions of tokens. In: Proceedings of the 39th International Conference on Machine Learning (ICML), pp 2206–2240 [find](https://scholar.google.com/scholar?q=Borgeaud+S%2C+Mensch+A%2C+Hoffmann+J%2C+et+al+%282022%29+Improving+language+models+by+retrieving+from+trillions+of+tokens%2E+In%3A+Proceedings+of+the+39th+International+Conference+on+Machine+Learning+%28ICML%29%2C+pp+220)
13. Brown TB, Mann B, Ryder N, et al (2020) [Language models are few-shot learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/). In: Advances in Neural Information Processing Systems 33 (NeurIPS)
14. Cai D, Wang Y, Li H, et al (2021) Neural machine translation with monolingual translation memory. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL/IJCNLP), pp 7307–7318 [find](https://scholar.google.com/scholar?q=Cai+D%2C+Wang+Y%2C+Li+H%2C+et+al+%282021%29+Neural+machine+translation+with+monolingual+translation+memory%2E+In%3A+Proceedings+of+the+59th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+and+the+11)
15. Castelli V, Chakravarti R, Dana S, et al (2020) The techqa dataset. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pp 1269–1278 [find](https://scholar.google.com/scholar?q=Castelli+V%2C+Chakravarti+R%2C+Dana+S%2C+et+al+%282020%29+The+techqa+dataset%2E+In%3A+Proceedings+of+the+58th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28ACL%29%2C+pp+1269%E2%80%931278)
16. Chalkidis I, Fergadiotis M, Malakasiotis P, et al (2020) [LEGAL-BERT: the muppets straight out of law school](https://md.research.elamur.ai/free/en/papers/chalkidis-2020-legal-bert-muppets-straight-out/). In: Findings of the Association for Computational Linguistics: EMNLP, pp 2898–2904
17. Chang Z, Li M, Jia X, et al (2025) One shot dominance: Knowledge poisoning attack on retrieval-augmented generation systems. In: Findings of the Association for Computational Linguistics: EMNLP, pp 18811–18825 [find](https://scholar.google.com/scholar?q=Chang+Z%2C+Li+M%2C+Jia+X%2C+et+al+%282025%29+One+shot+dominance%3A+Knowledge+poisoning+attack+on+retrieval%2Daugmented+generation+systems%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP%2C+pp+18)
18. Chen J, Chen Q, Li D, et al (2022a) Sedr: Segment representation learning for long documents dense retrieval. CoRR abs/2211.10841 [find](https://scholar.google.com/scholar?q=Chen+J%2C+Chen+Q%2C+Li+D%2C+et+al+%282022a%29+Sedr%3A+Segment+representation+learning+for+long+documents+dense+retrieval%2E+CoRR+abs%2F2211%2E10841)
19. Chen J, Lin H, Han X, et al (2024) [Benchmarking large language models in retrieval-augmented generation](https://md.research.elamur.ai/free/en/papers/chen-2023-benchmarking-large-language-models-retrieval/). In: Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), pp 17754–17762
20. Chen W, Wang X, Wang WY (2021) A dataset for answering time-sensitive questions. In: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks) [find](https://scholar.google.com/scholar?q=Chen+W%2C+Wang+X%2C+Wang+WY+%282021%29+A+dataset+for+answering+time%2Dsensitive+questions%2E+In%3A+Proceedings+of+the+Neural+Information+Processing+Systems+Track+on+Datasets+and+Benchmarks+%28NeurIPS+Datasets+and+Ben)
21. Chen X, Li L, Zhang N, et al (2022b) Decoupling knowledge from memorization: Retrieval-augmented prompt learning. In: Advances in Neural Information Processing Systems 35 (NeurIPS) [find](https://scholar.google.com/scholar?q=Chen+X%2C+Li+L%2C+Zhang+N%2C+et+al+%282022b%29+Decoupling+knowledge+from+memorization%3A+Retrieval%2Daugmented+prompt+learning%2E+In%3A+Advances+in+Neural+Information+Processing+Systems+35+%28NeurIPS%29)
22. Chen Y, Li H, Sui Y, et al (2025a) [Can indirect prompt injection attacks be detected and removed?](https://md.research.elamur.ai/free/en/papers/chen-2025-can-indirect-prompt-injection-attacks/) In: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL), pp 18189–18206
23. Chen Y, Wang Y, Zhang H, et al (2025b) Fine-grained privacy extraction from retrieval-augmented generation systems via knowledge asymmetry exploitation. CoRR abs/2507.23229 — [arXiv:2507.23229](https://arxiv.org/abs/2507.23229) [find](https://scholar.google.com/scholar?q=Chen+Y%2C+Wang+Y%2C+Zhang+H%2C+et+al+%282025b%29+Fine%2Dgrained+privacy+extraction+from+retrieval%2Daugmented+generation+systems+via+knowledge+asymmetry+exploitation%2E+CoRR+abs%2F2507%2E23229+%E2%80%94+%5BarXiv%3A2507%2E23229%5D%28https%3A)
24. Chen Y, Xiong Y, Wu S, et al (2025c) Beyond semantic similarity: Reducing unnecessary API calls via behavior-aligned retriever. CoRR abs/2508.14323. https://doi.org/10.48550/ARXIV.2508.14323 — [doi:10.48550/arxiv.2508.14323](https://doi.org/10.48550/arxiv.2508.14323) [↗](https://doi.org/10.48550/ARXIV.2508.14323)
25. Chen Y, Xiong Y, Wu S, et al (2026) Refilter: Improving robustness of retrieval-augmented generation via gated filter. arXiv preprint arXiv:260212709 [find](https://scholar.google.com/scholar?q=Chen+Y%2C+Xiong+Y%2C+Wu+S%2C+et+al+%282026%29+Refilter%3A+Improving+robustness+of+retrieval%2Daugmented+generation+via+gated+filter%2E+arXiv+preprint+arXiv%3A260212709)
26. Cheng Q, Li X, Li S, et al (2024) Unified active retrieval for retrieval augmented generation. In: Findings of the Association for Computational Linguistics: EMNLP, pp 17153–17166, https://doi.org/10.18653/V1/2024.FINDINGS-EMNLP.999 [↗](https://doi.org/10.18653/V1/2024.FINDINGS-EMNLP.999)
27. Cheng X, Lin Y, Chen X, et al (2023a) Decouple knowledge from paramters for plug-and-play language modeling. In: Findings of the Association for Computational Linguistics: ACL, pp 14288–14308 [find](https://scholar.google.com/scholar?q=Cheng+X%2C+Lin+Y%2C+Chen+X%2C+et+al+%282023a%29+Decouple+knowledge+from+paramters+for+plug%2Dand%2Dplay+language+modeling%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL%2C+pp+14288%E2%80%9314308)
28. Cheng X, Luo D, Chen X, et al (2023b) [Lift yourself up: Retrieval-augmented text generation with self-memory](https://md.research.elamur.ai/free/en/papers/cheng-2023-lift-yourself-up-retrieval-augmented/). In: Advances in Neural Information Processing Systems 36 (NeurIPS)
29. Chevalier A, Wettig A, Ajith A, et al (2023) [Adapting language models to compress contexts](https://md.research.elamur.ai/free/en/papers/chevalier-2023-adapting-language-models-compress-contexts/). In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 3829–3846, https://doi.org/10.18653/V1/2023. EMNLP-MAIN.232 [↗](https://doi.org/10.18653/V1/2023)
30. Chuang Y, Fang W, Li S, et al (2023) Expand, rerank, and retrieve: Query reranking for open-domain question answering. In: Findings of the Association for Computational Linguistics: ACL, pp 12131–12147 [find](https://scholar.google.com/scholar?q=Chuang+Y%2C+Fang+W%2C+Li+S%2C+et+al+%282023%29+Expand%2C+rerank%2C+and+retrieve%3A+Query+reranking+for+open%2Ddomain+question+answering%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL%2C+pp+12131%E2%80%931214)
31. Clark K, Luong M, Le QV, et al (2020) ELECTRA: pre-training text encoders as discriminators rather than generators. In: Proceedings of the 8th International Conference on Learning Representations (ICLR). [find](https://scholar.google.com/scholar?q=Clark+K%2C+Luong+M%2C+Le+QV%2C+et+al+%282020%29+ELECTRA%3A+pre%2Dtraining+text+encoders+as+discriminators+rather+than+generators%2E+In%3A+Proceedings+of+the+8th+International+Conference+on+Learning+Representations+%28ICL)
32. OpenReview.net Cohen S, Bitton R, Nassi B (2024) Unleashing worms and extracting data: Escalating the outcome of attacks against rag-based inference in scale and severity using jailbreaking. CoRR abs/2409.08045 — [arXiv:2409.08045](https://arxiv.org/abs/2409.08045) [find](https://scholar.google.com/scholar?q=OpenReview%2Enet+Cohen+S%2C+Bitton+R%2C+Nassi+B+%282024%29+Unleashing+worms+and+extracting+data%3A+Escalating+the+outcome+of+attacks+against+rag%2Dbased+inference+in+scale+and+severity+using+jailbreaking%2E+CoRR+abs%2F)
33. Colombo P, Pires TP, Boudiaf M, et al (2024) Saullm-7b: A pioneering large language model for law. CoRR abs/2403.03883 [find](https://scholar.google.com/scholar?q=Colombo+P%2C+Pires+TP%2C+Boudiaf+M%2C+et+al+%282024%29+Saullm%2D7b%3A+A+pioneering+large+language+model+for+law%2E+CoRR+abs%2F2403%2E03883)
34. Cui Y, Liu Z, Chen Y, et al (2023) Retrieval-augmented multiple instance learning. In: Advances in Neural Information Processing Systems 36 (NeurIPS) [find](https://scholar.google.com/scholar?q=Cui+Y%2C+Liu+Z%2C+Chen+Y%2C+et+al+%282023%29+Retrieval%2Daugmented+multiple+instance+learning%2E+In%3A+Advances+in+Neural+Information+Processing+Systems+36+%28NeurIPS%29)
35. Dai Y, Zhang Z, Liu Q, et al (2023) Simple and scalable nearest neighbor machine translation. In: Proceedings of The Eleventh International Conference on Learning Representations (ICLR) [find](https://scholar.google.com/scholar?q=Dai+Y%2C+Zhang+Z%2C+Liu+Q%2C+et+al+%282023%29+Simple+and+scalable+nearest+neighbor+machine+translation%2E+In%3A+Proceedings+of+The+Eleventh+International+Conference+on+Learning+Representations+%28ICLR%29)
36. Dale D, Voita E, Barrault L, et al (2023) [Detecting and mitigating hallucinations in machine translation: Model internal workings alone do well, sentence similarity even better](https://md.research.elamur.ai/free/en/papers/dale-2023-detecting-mitigating-hallucinations-machine-translation/). In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), pp 36–50
37. Databricks (2025a) Mosaic ai vector search. [https://docs.databricks.com/gcp/en/](https://docs.databricks.com/gcp/en/) vector-search/vector-search, accessed: 2026-03-04 [↗](https://docs.databricks.com/gcp/en/)
38. Databricks (2025b) [Vector search retrieval quality guide](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/). URL [https://docs.databricks.com/aws/en/vector-search/vector-search-retrieval-quality](https://docs.databricks.com/aws/en/vector-search/vector-search-retrieval-quality) DeepSeek-AI (2025) Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. CoRR abs/2501.12948 deepset (2026) Haystack documenta… [↗](https://docs.databricks.com/aws/en/vector-search/vector-search-retrieval-quality)
39. Deguchi H, Watanabe T, Matsui Y, et al (2023) Subset retrieval nearest neighbor machine translation. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), pp 174–189 [find](https://scholar.google.com/scholar?q=Deguchi+H%2C+Watanabe+T%2C+Matsui+Y%2C+et+al+%282023%29+Subset+retrieval+nearest+neighbor+machine+translation%2E+In%3A+Proceedings+of+the+61st+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28ACL%29%2C+)
40. Dettmers T, Pagnoni A, Holtzman A, et al (2023) [Qlora: Efficient finetuning of quantized llms](https://md.research.elamur.ai/free/en/papers/dettmers-2023-qlora-efficient-finetuning-quantized-llms/). In: Advances in Neural Information Processing Systems 36 (NeurIPS)
41. Devlin J, Chang M, Lee K, et al (2019) [BERT: pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pp 4171–4186
42. Ding Y, Fan W, Ning L, et al (2024) [A survey on RAG meets llms: Towards retrieval-augmented large language models](https://md.research.elamur.ai/free/en/papers/fan-2024-survey-rag-meeting-llms-towards/). CoRR abs/2405.06211
43. Ding Z, Jiang G, Zhang S, et al (2023) SKDBERT: compressing BERT via stochastic knowledge distillation. In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), pp 7414–7422 [find](https://scholar.google.com/scholar?q=Ding+Z%2C+Jiang+G%2C+Zhang+S%2C+et+al+%282023%29+SKDBERT%3A+compressing+BERT+via+stochastic+knowledge+distillation%2E+In%3A+Proceedings+of+the+Thirty%2DSeventh+AAAI+Conference+on+Artificial+Intelligence+%28AAAI%29%2C+pp+7414)
44. Doostmohammadi E, Norlund T, Kuhlmann M, et al (2023) Surface-based retrieval reduces perplexity of retrieval-augmented language models. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), pp 521–529, https://doi.org/10.18653/V1/2023.ACL-SHORT.45 [↗](https://doi.org/10.18653/V1/2023.ACL-SHORT.45)
45. Douze M, Guzhva A, Deng C, et al (2024) [The faiss library](https://md.research.elamur.ai/free/en/papers/douze-2024-faiss-library/). CoRR abs/2401.08281
46. DSPy (2026) Tutorial: Retrieval-augmented generation (rag) – dspy. [https://dspy.ai/](https://dspy.ai/) tutorials/rag/, accessed: 2026-03-04 [↗](https://dspy.ai/)
47. Dubois Y, Galambosi B, Liang P, et al (2024) Length-controlled alpacaeval: A simple way to debias automatic evaluators. CoRR abs/2404.04475 [find](https://scholar.google.com/scholar?q=Dubois+Y%2C+Galambosi+B%2C+Liang+P%2C+et+al+%282024%29+Length%2Dcontrolled+alpacaeval%3A+A+simple+way+to+debias+automatic+evaluators%2E+CoRR+abs%2F2404%2E04475)
48. Edge D, Trinh H, Cheng N, et al (2024) From local to global: A graph RAG approach to query-focused summarization. CoRR abs/2404.16130 [find](https://scholar.google.com/scholar?q=Edge+D%2C+Trinh+H%2C+Cheng+N%2C+et+al+%282024%29+From+local+to+global%3A+A+graph+RAG+approach+to+query%2Dfocused+summarization%2E+CoRR+abs%2F2404%2E16130)
49. ES S, James J, Anke LE, et al (2024) [Ragas: Automated evaluation of retrieval augmented generation](https://md.research.elamur.ai/free/en/papers/es-2023-ragas-automated-evaluation-retrieval-augmented/). In: Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL), pp 150–158 explosion (2016) Spacy. misc, URL [https://spacy.io/](https://spacy.io/) [↗](https://spacy.io/)
50. Fabbri AR, Ng P, Wang Z, et al (2020) Template-based question generation from retrieved sentences for improved unsupervised question answering. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pp 4508–4513 [find](https://scholar.google.com/scholar?q=Fabbri+AR%2C+Ng+P%2C+Wang+Z%2C+et+al+%282020%29+Template%2Dbased+question+generation+from+retrieved+sentences+for+improved+unsupervised+question+answering%2E+In%3A+Proceedings+of+the+58th+Annual+Meeting+of+the+Associ)
51. Fabbri AR, Kryscinski W, McCann B, et al (2021) Summeval: Re-evaluating summarization evaluation. Transactions of the Association for Computational Linguistics 9:391–409 [find](https://scholar.google.com/scholar?q=Fabbri+AR%2C+Kryscinski+W%2C+McCann+B%2C+et+al+%282021%29+Summeval%3A+Re%2Devaluating+summarization+evaluation%2E+Transactions+of+the+Association+for+Computational+Linguistics+9%3A391%E2%80%93409)
52. Facebook (2013) RocksDB. Software, URL [https://github.com/facebook/rocksdb](https://github.com/facebook/rocksdb) [↗](https://github.com/facebook/rocksdb)
53. Fan A, Gardent C (2022) Generating full length wikipedia biographies: The impact of gender bias on the retrieval-based generation of women biographies. CoRR abs/2204.05879 — [arXiv:2204.05879](https://arxiv.org/abs/2204.05879) [find](https://scholar.google.com/scholar?q=Fan+A%2C+Gardent+C+%282022%29+Generating+full+length+wikipedia+biographies%3A+The+impact+of+gender+bias+on+the+retrieval%2Dbased+generation+of+women+biographies%2E+CoRR+abs%2F2204%2E05879+%E2%80%94+%5BarXiv%3A2204%2E05879%5D%28https%3A%2F)
54. Fan A, Gardent C, Braud C, et al (2021) [Augmenting transformers with knn-based composite memory for dialog](https://md.research.elamur.ai/free/en/papers/fan-2021-augmenting-transformers-knn-based-composite/). Transactions of the Association for Computational Linguistics 9:82–99
55. Févry T, Soares LB, FitzGerald N, et al (2020) [Entities as experts: Sparse memory access with entity supervision](https://md.research.elamur.ai/free/en/papers/fevry-2020-entities-as-experts-sparse-memory/). In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 4937–4951
56. Friel R, Belyi M, Sanyal A (2024) Ragbench: Explainable benchmark for retrieval-augmented generation systems. CoRR abs/2407.11005 — [arXiv:2407.11005](https://arxiv.org/abs/2407.11005) [find](https://scholar.google.com/scholar?q=Friel+R%2C+Belyi+M%2C+Sanyal+A+%282024%29+Ragbench%3A+Explainable+benchmark+for+retrieval%2Daugmented+generation+systems%2E+CoRR+abs%2F2407%2E11005+%E2%80%94+%5BarXiv%3A2407%2E11005%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2407%2E11005%29)
57. Fu Y, Chen C, Chen X, et al (2024) Optimizing the number of clusters for billion-scale quantization-based nearest neighbor search. IEEE Transactions on Knowledge and Data Engineering 36(11):6786–6800 [find](https://scholar.google.com/scholar?q=Fu+Y%2C+Chen+C%2C+Chen+X%2C+et+al+%282024%29+Optimizing+the+number+of+clusters+for+billion%2Dscale+quantization%2Dbased+nearest+neighbor+search%2E+IEEE+Transactions+on+Knowledge+and+Data+Engineering+36%2811%29%3A6786%E2%80%936800)
58. Ganesh P, Chen Y, Lou X, et al (2021) [Compressing large-scale transformer-based models: A case study on BERT](https://md.research.elamur.ai/free/en/papers/ganesh-2021-compressing-large-scale-transformer-based/). Transactions of the Association for Computational Linguistics 9:1061–1080
59. Gao L, Dai Z, Pasupat P, et al (2023a) [RARR: researching and revising what language models say, using language models](https://md.research.elamur.ai/free/en/papers/gao-2023-rarr-researching-revising-what-language/). In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), pp 16477–16508, https://doi.org/10.18653/V1/2023.ACL-LONG.910 [↗](https://doi.org/10.18653/V1/2023.ACL-LONG.910)
60. Gao T, Yao X, Chen D (2021) [Simcse: Simple contrastive learning of sentence embeddings](https://md.research.elamur.ai/free/en/papers/gao-2021-simcse-simple-contrastive-learning-sentence/). In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 6894–6910
61. Gao Y, Xiong Y, Gao X, et al (2023b) [Retrieval-augmented generation for large language models: A survey](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/). CoRR abs/2312.10997
62. Glass MR, Wu X, Naik AR, et al (2023) Retrieval-based transformer for table augmentation. In: Findings of the Association for Computational Linguistics: ACL, pp 5635–5648 [find](https://scholar.google.com/scholar?q=Glass+MR%2C+Wu+X%2C+Naik+AR%2C+et+al+%282023%29+Retrieval%2Dbased+transformer+for+table+augmentation%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL%2C+pp+5635%E2%80%935648)
63. Gong H, Shen Y, Yu D, et al (2020) Recurrent chunking mechanisms for long-text machine reading comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pp 6751–6761 [find](https://scholar.google.com/scholar?q=Gong+H%2C+Shen+Y%2C+Yu+D%2C+et+al+%282020%29+Recurrent+chunking+mechanisms+for+long%2Dtext+machine+reading+comprehension%2E+In%3A+Proceedings+of+the+58th+Annual+Meeting+of+the+Association+for+Computational+Linguistic)
64. Google Cloud (2026a) Use data ingestion with vertex ai rag engine. [https://docs.cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/use-data-ingestion](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/use-data-ingestion), accessed: 2026-03-04 [↗](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/use-data-ingestion)
65. Google Cloud (2026b) Vertex ai rag engine billing. [https://docs.cloud.google.com/](https://docs.cloud.google.com/) vertex-ai/generative-ai/docs/rag-engine/rag-engine-billing, accessed: 2026-03-04 [↗](https://docs.cloud.google.com/)
66. Gunawardana A, Shani G (2009) A survey of accuracy evaluation metrics of recommendation tasks. Journal of Machine Learning Research 10:2935–2962 [find](https://scholar.google.com/scholar?q=Gunawardana+A%2C+Shani+G+%282009%29+A+survey+of+accuracy+evaluation+metrics+of+recommendation+tasks%2E+Journal+of+Machine+Learning+Research+10%3A2935%E2%80%932962)
67. Guo D, Tang D, Duan N, et al (2019) Coupling retrieval and meta-learning for context-dependent semantic parsing. In: Proceedings of the 57th Conference of the Association for Computational Linguistics (ACL), pp 855–866 [find](https://scholar.google.com/scholar?q=Guo+D%2C+Tang+D%2C+Duan+N%2C+et+al+%282019%29+Coupling+retrieval+and+meta%2Dlearning+for+context%2Ddependent+semantic+parsing%2E+In%3A+Proceedings+of+the+57th+Conference+of+the+Association+for+Computational+Linguistics)
68. Guo R, Sun P, Lindgren E, et al (2020) Accelerating large-scale inference with anisotropic vector quantization. In: Proceedings of the 37th International Conference on Machine Learning (ICML), pp 3887–3896 [find](https://scholar.google.com/scholar?q=Guo+R%2C+Sun+P%2C+Lindgren+E%2C+et+al+%282020%29+Accelerating+large%2Dscale+inference+with+anisotropic+vector+quantization%2E+In%3A+Proceedings+of+the+37th+International+Conference+on+Machine+Learning+%28ICML%29%2C+pp+3887)
69. Guo R, Luan X, Xiang L, et al (2022) Manu: A cloud native vector database management system. Proceedings of the VLDB Endowment 15(12):3548–3561 [find](https://scholar.google.com/scholar?q=Guo+R%2C+Luan+X%2C+Xiang+L%2C+et+al+%282022%29+Manu%3A+A+cloud+native+vector+database+management+system%2E+Proceedings+of+the+VLDB+Endowment+15%2812%29%3A3548%E2%80%933561)
70. Guo Z, Cheng S, Wang Y, et al (2023) Prompt-guided retrieval augmentation for non-knowledge-intensive tasks. In: Findings of the Association for Computational Linguistics: ACL, pp 10896–10912 [find](https://scholar.google.com/scholar?q=Guo+Z%2C+Cheng+S%2C+Wang+Y%2C+et+al+%282023%29+Prompt%2Dguided+retrieval+augmentation+for+non%2Dknowledge%2Dintensive+tasks%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL%2C+pp+10896%E2%80%9310912)
71. Guu K, Lee K, Tung Z, et al (2020) [Retrieval augmented language model pre-training](https://md.research.elamur.ai/free/en/papers/guu-2020-realm-retrieval-augmented-language-model/). In: Proceedings of the 37th International Conference on Machine Learning (ICML), pp 3929–3938
72. Harris D, Harris S (2010) Digital design and computer architecture. [find](https://scholar.google.com/scholar?q=Harris+D%2C+Harris+S+%282010%29+Digital+design+and+computer+architecture%2E)
73. Morgan Kaufmann Harris ZS (1954) Distributional structure. Word 10(2-3):146–162 [find](https://scholar.google.com/scholar?q=Morgan+Kaufmann+Harris+ZS+%281954%29+Distributional+structure%2E+Word+10%282%2D3%29%3A146%E2%80%93162)
74. Hatalis K, Christou D, Myers J, et al (2023) Memory matters: The need to improve long-term memory in llm-agents. In: Proceedings of the AAAI Symposium Series, pp 277–280 [find](https://scholar.google.com/scholar?q=Hatalis+K%2C+Christou+D%2C+Myers+J%2C+et+al+%282023%29+Memory+matters%3A+The+need+to+improve+long%2Dterm+memory+in+llm%2Dagents%2E+In%3A+Proceedings+of+the+AAAI+Symposium+Series%2C+pp+277%E2%80%93280)
75. He Q, Wang Y, Wang W (2024) Can language models act as knowledge bases at scale? CoRR abs/2402.14273 — [arXiv:2402.14273](https://arxiv.org/abs/2402.14273) [find](https://scholar.google.com/scholar?q=He+Q%2C+Wang+Y%2C+Wang+W+%282024%29+Can+language+models+act+as+knowledge+bases+at+scale%3F+CoRR+abs%2F2402%2E14273+%E2%80%94+%5BarXiv%3A2402%2E14273%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2402%2E14273%29)
76. Hoffmann J, Borgeaud S, Mensch A, et al (2022) [Training compute-optimal large language models](https://md.research.elamur.ai/free/en/papers/hoffmann-2022-training-compute-optimal-large-language/). CoRR abs/2203.15556
77. Hofstätter S, Chen J, Raman K, et al (2023) Fid-light: Efficient and effective retrieval-augmented text generation. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). ACM, pp 1437–1447 [find](https://scholar.google.com/scholar?q=Hofsta%CC%88tter+S%2C+Chen+J%2C+Raman+K%2C+et+al+%282023%29+Fid%2Dlight%3A+Efficient+and+effective+retrieval%2Daugmented+text+generation%2E+In%3A+Proceedings+of+the+46th+International+ACM+SIGIR+Conference+on+Research+and+Deve)
78. Holtzman A, Buys J, Du L, et al (2020) [The curious case of neural text degeneration](https://md.research.elamur.ai/free/en/papers/holtzman-2019-curious-case-neural-text-degeneration/). In: Proceedings of the 8th International Conference on Learning Representations (ICLR)
79. Hoshi Y, Miyashita D, Ng Y, et al (2023) Ralle: A framework for developing and evaluating retrieval-augmented large language models. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 52–69 [find](https://scholar.google.com/scholar?q=Hoshi+Y%2C+Miyashita+D%2C+Ng+Y%2C+et+al+%282023%29+Ralle%3A+A+framework+for+developing+and+evaluating+retrieval%2Daugmented+large+language+models%2E+In%3A+Proceedings+of+the+2023+Conference+on+Empirical+Methods+in+Natu)
80. Hossain N, Ghazvininejad M, Zettlemoyer L (2020) Simple and effective retrieve-edit-rerank text generation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pp 2532–2538, https://doi.org/ 10.18653/V1/2020.ACL-MAIN.228 [find](https://scholar.google.com/scholar?q=Hossain+N%2C+Ghazvininejad+M%2C+Zettlemoyer+L+%282020%29+Simple+and+effective+retrieve%2Dedit%2Drerank+text+generation%2E+In%3A+Proceedings+of+the+58th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+)
81. Hu EJ, Shen Y, Wallis P, et al (2022) [Lora: Low-rank adaptation of large language models](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/). In: Proceedings of The Tenth International Conference on Learning Representations (ICLR)
82. Hu W, Gu J, Dou Z, et al (2025a) Mrag-bench: Vision-centric evaluation for retrieval-augmented multimodal models. In: Proceedings of The Thirteenth International Conference on Learning Representations (ICLR) [find](https://scholar.google.com/scholar?q=Hu+W%2C+Gu+J%2C+Dou+Z%2C+et+al+%282025a%29+Mrag%2Dbench%3A+Vision%2Dcentric+evaluation+for+retrieval%2Daugmented+multimodal+models%2E+In%3A+Proceedings+of+The+Thirteenth+International+Conference+on+Learning+Representations)
83. Hu X (2023) Multimodal named entity recognition and relation extraction with retrieval-augmented strategy. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). ACM, p 3488 [find](https://scholar.google.com/scholar?q=Hu+X+%282023%29+Multimodal+named+entity+recognition+and+relation+extraction+with+retrieval%2Daugmented+strategy%2E+In%3A+Proceedings+of+the+46th+International+ACM+SIGIR+Conference+on+Research+and+Development+in)
84. Hu Y, Lu Y (2024) RAG and RAU: A survey on retrieval-augmented language model in natural language processing. CoRR abs/2404.19543 — [arXiv:2404.19543](https://arxiv.org/abs/2404.19543) [find](https://scholar.google.com/scholar?q=Hu+Y%2C+Lu+Y+%282024%29+RAG+and+RAU%3A+A+survey+on+retrieval%2Daugmented+language+model+in+natural+language+processing%2E+CoRR+abs%2F2404%2E19543+%E2%80%94+%5BarXiv%3A2404%2E19543%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2404%2E19543%29)
85. Hu Z, Murthy V, Pan Z, et al (2025b) Hedrarag: Coordinating LLM generation and database retrieval in heterogeneous RAG serving. CoRR abs/2507.09138 [find](https://scholar.google.com/scholar?q=Hu+Z%2C+Murthy+V%2C+Pan+Z%2C+et+al+%282025b%29+Hedrarag%3A+Coordinating+LLM+generation+and+database+retrieval+in+heterogeneous+RAG+serving%2E+CoRR+abs%2F2507%2E09138)
86. Huang J, Ping W, Xu P, et al (2023a) RAVEN: in-context learning with retrieval augmented encoder-decoder language models. CoRR abs/2308.07922 — [arXiv:2308.07922](https://arxiv.org/abs/2308.07922) [find](https://scholar.google.com/scholar?q=Huang+J%2C+Ping+W%2C+Xu+P%2C+et+al+%282023a%29+RAVEN%3A+in%2Dcontext+learning+with+retrieval+augmented+encoder%2Ddecoder+language+models%2E+CoRR+abs%2F2308%2E07922+%E2%80%94+%5BarXiv%3A2308%2E07922%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2308%2E07922%29)
87. Huang Q, Tung AKH (2023) Lightweight-yet-efficient: Revitalizing ball-tree for point-to-hyperplane nearest neighbor search. In: Proceedings of the 39th IEEE International Conference on Data Engineering (ICDE), pp 436–449 [find](https://scholar.google.com/scholar?q=Huang+Q%2C+Tung+AKH+%282023%29+Lightweight%2Dyet%2Defficient%3A+Revitalizing+ball%2Dtree+for+point%2Dto%2Dhyperplane+nearest+neighbor+search%2E+In%3A+Proceedings+of+the+39th+IEEE+International+Conference+on+Data+Engineerin)
88. Huang Q, Fu S, Liu X, et al (2023b) Learning retrieval augmentation for personalized dialogue generation. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 2523–2540 [find](https://scholar.google.com/scholar?q=Huang+Q%2C+Fu+S%2C+Liu+X%2C+et+al+%282023b%29+Learning+retrieval+augmentation+for+personalized+dialogue+generation%2E+In%3A+Proceedings+of+the+2023+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28EM)
89. Huang X, Liu W, Chen X, et al (2024) Understanding the planning of LLM agents: A survey. CoRR abs/2402.02716 [find](https://scholar.google.com/scholar?q=Huang+X%2C+Liu+W%2C+Chen+X%2C+et+al+%282024%29+Understanding+the+planning+of+LLM+agents%3A+A+survey%2E+CoRR+abs%2F2402%2E02716)
90. Huang Y, Huang J (2024) A survey on retrieval-augmented text generation for large language models. CoRR abs/2404.10981 — [arXiv:2404.10981](https://arxiv.org/abs/2404.10981) [find](https://scholar.google.com/scholar?q=Huang+Y%2C+Huang+J+%282024%29+A+survey+on+retrieval%2Daugmented+text+generation+for+large+language+models%2E+CoRR+abs%2F2404%2E10981+%E2%80%94+%5BarXiv%3A2404%2E10981%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2404%2E10981%29)
91. Huang Y, Liu D, Zhong Z, et al (2023c) knn-adapter: Efficient domain adaptation for black-box language models. CoRR abs/2302.10879. https://doi.org/10.48550/ ARXIV.2302.10879 — [arXiv:2302.10879](https://arxiv.org/abs/2302.10879) [find](https://scholar.google.com/scholar?q=Huang+Y%2C+Liu+D%2C+Zhong+Z%2C+et+al+%282023c%29+knn%2Dadapter%3A+Efficient+domain+adaptation+for+black%2Dbox+language+models%2E+CoRR+abs%2F2302%2E10879%2E+https%3A%2F%2Fdoi%2Eorg%2F10%2E48550%2F+ARXIV%2E2302%2E10879+%E2%80%94+%5BarXiv%3A2302%2E10879%5D%28http)
92. Hui Y, Lu Y, Zhang H (2024) UDA: A benchmark suite for retrieval augmented generation in real-world document analysis. In: Advances in Neural Information Processing Systems 38 (NeurIPS) [find](https://scholar.google.com/scholar?q=Hui+Y%2C+Lu+Y%2C+Zhang+H+%282024%29+UDA%3A+A+benchmark+suite+for+retrieval+augmented+generation+in+real%2Dworld+document+analysis%2E+In%3A+Advances+in+Neural+Information+Processing+Systems+38+%28NeurIPS%29)
93. Ishiwatari S, Yao J, Liu S, et al (2017) Chunk-based decoder for neural machine translation. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), pp 1901–1912 [find](https://scholar.google.com/scholar?q=Ishiwatari+S%2C+Yao+J%2C+Liu+S%2C+et+al+%282017%29+Chunk%2Dbased+decoder+for+neural+machine+translation%2E+In%3A+Proceedings+of+the+55th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28ACL%29%2C+pp+1901%E2%80%93)
94. Izacard G, Grave E (2021) [Leveraging passage retrieval with generative models for open domain question answering](https://md.research.elamur.ai/free/en/papers/izacard-2021-leveraging-passage-retrieval-generative-models/). In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL), pp 874–880
95. Izacard G, Caron M, Hosseini L, et al (2022) [Unsupervised dense information retrieval with contrastive learning](https://md.research.elamur.ai/free/en/papers/izacard-2021-unsupervised-dense-information-retrieval-contrastive/). IEEE Transactions on Pattern Analysis and Machine Intelligence 2022
96. Izacard G, Lewis PSH, Lomeli M, et al (2023) [Atlas: Few-shot learning with retrieval augmented language models](https://md.research.elamur.ai/free/en/papers/izacard-2022-atlas-few-shot-learning-retrieval/). Journal of Machine Learning Research 24:251:1–251:43
97. Jégou H, Douze M, Schmid C (2011) Product quantization for nearest neighbor search. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(1):117–128 [find](https://scholar.google.com/scholar?q=Je%CC%81gou+H%2C+Douze+M%2C+Schmid+C+%282011%29+Product+quantization+for+nearest+neighbor+search%2E+IEEE+Transactions+on+Pattern+Analysis+and+Machine+Intelligence+33%281%29%3A117%E2%80%93128)
98. Ji X, Glenn P, Parameswaran AG, et al (2025) TARGET: benchmarking table retrieval for generative tasks. CoRR abs/2505.11545 — [arXiv:2505.11545](https://arxiv.org/abs/2505.11545) [find](https://scholar.google.com/scholar?q=Ji+X%2C+Glenn+P%2C+Parameswaran+AG%2C+et+al+%282025%29+TARGET%3A+benchmarking+table+retrieval+for+generative+tasks%2E+CoRR+abs%2F2505%2E11545+%E2%80%94+%5BarXiv%3A2505%2E11545%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2505%2E11545%29)
99. Ji Z, Lee N, Frieske R, et al (2023a) [Survey of hallucination in natural language generation](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/). ACM Computing Surveys 55(12):248:1–248:38
100. Ji Z, Liu Z, Lee N, et al (2023b) RHO: reducing hallucination in open-domain dialogues with knowledge grounding. In: Findings of the Association for Computational Linguistics: ACL, pp 4504–4522 [find](https://scholar.google.com/scholar?q=Ji+Z%2C+Liu+Z%2C+Lee+N%2C+et+al+%282023b%29+RHO%3A+reducing+hallucination+in+open%2Ddomain+dialogues+with+knowledge+grounding%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL%2C+pp+4504%E2%80%934522)
101. Jiang AQ, Sablayrolles A, Mensch A, et al (2023a) Mistral 7b. CoRR abs/2310.06825 [find](https://scholar.google.com/scholar?q=Jiang+AQ%2C+Sablayrolles+A%2C+Mensch+A%2C+et+al+%282023a%29+Mistral+7b%2E+CoRR+abs%2F2310%2E06825)
102. Jiang AQ, Sablayrolles A, Roux A, et al (2024a) Mixtral of experts. CoRR abs/2401.04088. https://doi.org/10.48550/ARXIV.2401.04088 [↗](https://doi.org/10.48550/ARXIV.2401.04088)
103. Jiang H, Lu Z, Meng F, et al (2022) Towards robust k-nearest-neighbor machine translation. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 5468–5477 [find](https://scholar.google.com/scholar?q=Jiang+H%2C+Lu+Z%2C+Meng+F%2C+et+al+%282022%29+Towards+robust+k%2Dnearest%2Dneighbor+machine+translation%2E+In%3A+Proceedings+of+the+2022+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%29%2C+pp+5468%E2%80%935)
104. Jiang W, Zhang S, Han B, et al (2024b) Piperag: Fast retrieval-augmented generation via algorithm-system co-design. CoRR abs/2403.05676 — [arXiv:2403.05676](https://arxiv.org/abs/2403.05676) [find](https://scholar.google.com/scholar?q=Jiang+W%2C+Zhang+S%2C+Han+B%2C+et+al+%282024b%29+Piperag%3A+Fast+retrieval%2Daugmented+generation+via+algorithm%2Dsystem+co%2Ddesign%2E+CoRR+abs%2F2403%2E05676+%E2%80%94+%5BarXiv%3A2403%2E05676%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2403%2E05676%29)
105. Jiang W, Subramanian S, Graves C, et al (2025) RAGO: systematic performance optimization for retrieval-augmented generation serving. In: Proceedings of the 52nd Annual International Symposium on Computer Architecture (ISCA), pp 974–989 [find](https://scholar.google.com/scholar?q=Jiang+W%2C+Subramanian+S%2C+Graves+C%2C+et+al+%282025%29+RAGO%3A+systematic+performance+optimization+for+retrieval%2Daugmented+generation+serving%2E+In%3A+Proceedings+of+the+52nd+Annual+International+Symposium+on+Compu)
106. Jiang Z, Xu FF, Gao L, et al (2023b) [Active retrieval augmented generation](https://md.research.elamur.ai/free/en/papers/jiang-2023-active-retrieval-augmented-generation/). In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 7969–7992
107. Jiao X, Yin Y, Shang L, et al (2020) [Tinybert: Distilling BERT for natural language understanding](https://md.research.elamur.ai/free/en/papers/jiao-2020-tinybert-distilling-bert-natural-language/). In: Findings of the Association for Computational Linguistics: EMNLP, pp 4163–4174
108. Jin C, Zhang Z, Jiang X, et al (2024a) Ragcache: Efficient knowledge caching for retrieval-augmented generation. CoRR abs/2404.12457 [find](https://scholar.google.com/scholar?q=Jin+C%2C+Zhang+Z%2C+Jiang+X%2C+et+al+%282024a%29+Ragcache%3A+Efficient+knowledge+caching+for+retrieval%2Daugmented+generation%2E+CoRR+abs%2F2404%2E12457)
109. Jin J, Wang H, Zhang H, et al (2024b) DVD: dynamic contrastive decoding for knowledge amplification in multi-document question answering. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 4624–4637 [find](https://scholar.google.com/scholar?q=Jin+J%2C+Wang+H%2C+Zhang+H%2C+et+al+%282024b%29+DVD%3A+dynamic+contrastive+decoding+for+knowledge+amplification+in+multi%2Ddocument+question+answering%2E+In%3A+Proceedings+of+the+2024+Conference+on+Empirical+Methods+in)
110. Jin Q, Kim W, Chen Q, et al (2023) Medcpt: Contrastive pre-trained transformers with large-scale pubmed search logs for zero-shot biomedical information retrieval. Bioinformatics 39(10) [find](https://scholar.google.com/scholar?q=Jin+Q%2C+Kim+W%2C+Chen+Q%2C+et+al+%282023%29+Medcpt%3A+Contrastive+pre%2Dtrained+transformers+with+large%2Dscale+pubmed+search+logs+for+zero%2Dshot+biomedical+information+retrieval%2E+Bioinformatics+39%2810%29)
111. Johnson J, Douze M, Jégou H (2021) Billion-scale similarity search with gpus. IEEE Transactions on Big Data 7(3):535–547 de Jong M, Zemlyanskiy Y, FitzGerald N, et al (2022) Mention memory: incorporating textual knowledge into transformers through entity mention attention. In: Proceedings of The Te… [find](https://scholar.google.com/scholar?q=Johnson+J%2C+Douze+M%2C+Je%CC%81gou+H+%282021%29+Billion%2Dscale+similarity+search+with+gpus%2E+IEEE+Transactions+on+Big+Data+7%283%29%3A535%E2%80%93547+de+Jong+M%2C+Zemlyanskiy+Y%2C+FitzGerald+N%2C+et+al+%282022%29+Mention+memory%3A+incorpora)
112. Kaplan J, McCandlish S, Henighan T, et al (2020) [Scaling laws for neural language models](https://md.research.elamur.ai/free/en/papers/kaplan-2020-scaling-laws-neural-language-models/). CoRR abs/2001.08361
113. Karpukhin V, Oguz B, Min S, et al (2020) [Dense passage retrieval for open-domain question answering](https://md.research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/). In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 6769–6781
114. Khandelwal U, Levy O, Jurafsky D, et al (2020) Generalization through memorization: Nearest neighbor language models. In: Proceedings of The 8th International Conference on Learning Representations (ICLR) [find](https://scholar.google.com/scholar?q=Khandelwal+U%2C+Levy+O%2C+Jurafsky+D%2C+et+al+%282020%29+Generalization+through+memorization%3A+Nearest+neighbor+language+models%2E+In%3A+Proceedings+of+The+8th+International+Conference+on+Learning+Representations+%28I)
115. Khandelwal U, Fan A, Jurafsky D, et al (2021) Nearest neighbor machine translation. In: Proceedings of the 9th International Conference on Learning Representations (ICLR) [find](https://scholar.google.com/scholar?q=Khandelwal+U%2C+Fan+A%2C+Jurafsky+D%2C+et+al+%282021%29+Nearest+neighbor+machine+translation%2E+In%3A+Proceedings+of+the+9th+International+Conference+on+Learning+Representations+%28ICLR%29)
116. Kim K, Lee J (2024) RE-RAG: improving open-domain QA performance and interpretability with relevance estimator in retrieval-augmented generation. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 22149–22161, https://doi.org/10.18653/V1/2024. EMNL… [↗](https://doi.org/10.18653/V1/2024)
117. Kim S, Gholami A, Yao Z, et al (2021) [I-BERT: integer-only BERT quantization](https://md.research.elamur.ai/free/en/papers/kim-2021-i-bert-integer-only-bert/). In: Proceedings of the 38th International Conference on Machine Learning (ICML), pp 5506–5518
118. King B, Flanigan J (2023) Diverse retrieval-augmented in-context learning for dialogue state tracking. In: Findings of the Association for Computational Linguistics: ACL, pp 5570–5585 [find](https://scholar.google.com/scholar?q=King+B%2C+Flanigan+J+%282023%29+Diverse+retrieval%2Daugmented+in%2Dcontext+learning+for+dialogue+state+tracking%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL%2C+pp+5570%E2%80%935585)
119. Kwiatkowski T, Palomaki J, Redfield O, et al (2019) [Natural questions: a benchmark for question answering research](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/). Transactions of the Association for Computational Linguistics 7:452–466
120. Kwon W, Li Z, Zhuang S, et al (2023) [Efficient memory management for large language model serving with pagedattention](https://md.research.elamur.ai/free/en/papers/kwon-2023-efficient-memory-management-large-language/). In: Proceedings of the 29th Symposium on Operating Systems Principles (SOSP), pp 611–626, https://doi.org/10.1145/ 3600006.3613165
121. LangChain (2023) LangChain. Software, URL [https://www.langchain.com/](https://www.langchain.com/) [↗](https://www.langchain.com/)
122. LangChain (2026) Langsmith observability. [https://docs.langchain.com/langsmith/](https://docs.langchain.com/langsmith/) observability, accessed: 2026-03-04 [↗](https://docs.langchain.com/langsmith/)
123. Lazaridou A, Gribovskaya E, Stokowiec W, et al (2022) [Internet-augmented language models through few-shot prompting for open-domain question answering](https://md.research.elamur.ai/free/en/papers/lazaridou-2022-internet-augmented-language-models-through/). CoRR abs/2203.05115. https://doi.org/10.48550/ARXIV.2203.05115 [↗](https://doi.org/10.48550/ARXIV.2203.05115)
124. Lee K, Han S, Hwang S, et al (2023) When to read documents or QA history: On unified and selective open-domain QA. In: Findings of the Association for Computational Linguistics: ACL, pp 6420–6432 [find](https://scholar.google.com/scholar?q=Lee+K%2C+Han+S%2C+Hwang+S%2C+et+al+%282023%29+When+to+read+documents+or+QA+history%3A+On+unified+and+selective+open%2Ddomain+QA%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL%2C+pp+6420%E2%80%936432)
125. Lee S, Shakir A, Koenig D, et al (2024) Open source strikes bread - new fluffy embeddings model. URL [https://www.mixedbread.ai/blog/mxbai-embed-large-v1](https://www.mixedbread.ai/blog/mxbai-embed-large-v1) [↗](https://www.mixedbread.ai/blog/mxbai-embed-large-v1)
126. Lehman E, Hernandez E, Mahajan D, et al (2023) Do we still need clinical language models? In: Conference on Health, Inference, and Learning (CHIL), pp 578–597 [find](https://scholar.google.com/scholar?q=Lehman+E%2C+Hernandez+E%2C+Mahajan+D%2C+et+al+%282023%29+Do+we+still+need+clinical+language+models%3F+In%3A+Conference+on+Health%2C+Inference%2C+and+Learning+%28CHIL%29%2C+pp+578%E2%80%93597)
127. Lewis PSH, Perez E, Piktus A, et al (2020) [Retrieval-augmented generation for knowledge-intensive NLP tasks](https://md.research.elamur.ai/free/en/papers/lewis-2020-retrieval-augmented-generation-knowledge-intensive/). In: Advances in Neural Information Processing Systems 33 (NeurIPS)
128. Lewis PSH, Oguz B, Xiong W, et al (2022) Boosted dense retriever. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), pp 3102–3117 [find](https://scholar.google.com/scholar?q=Lewis+PSH%2C+Oguz+B%2C+Xiong+W%2C+et+al+%282022%29+Boosted+dense+retriever%2E+In%3A+Proceedings+of+the+2022+Conference+of+the+North+American+Chapter+of+the+Association+for+Computational+Linguistics%3A+Human+Language+)
129. Li H, Su Y, Cai D, et al (2022a) A survey on retrieval-augmented text generation. CoRR abs/2202.01110 — [arXiv:2202.01110](https://arxiv.org/abs/2202.01110) [find](https://scholar.google.com/scholar?q=Li+H%2C+Su+Y%2C+Cai+D%2C+et+al+%282022a%29+A+survey+on+retrieval%2Daugmented+text+generation%2E+CoRR+abs%2F2202%2E01110+%E2%80%94+%5BarXiv%3A2202%2E01110%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2202%2E01110%29)
130. Li H, Xu M, Song Y (2023a) Sentence embedding leaks more information than you expect: Generative embedding inversion attack to recover the whole sentence. In: Findings of the Association for Computational Linguistics: ACL, pp 14022–14040 [find](https://scholar.google.com/scholar?q=Li+H%2C+Xu+M%2C+Song+Y+%282023a%29+Sentence+embedding+leaks+more+information+than+you+expect%3A+Generative+embedding+inversion+attack+to+recover+the+whole+sentence%2E+In%3A+Findings+of+the+Association+for+Computati)
131. Li M, Wang Y, Zhang P, et al (2023b) Deep learning for approximate nearest neighbour search: A survey and future directions. IEEE Transactions on Knowledge and Data Engineering 35(9):8997–9018 [find](https://scholar.google.com/scholar?q=Li+M%2C+Wang+Y%2C+Zhang+P%2C+et+al+%282023b%29+Deep+learning+for+approximate+nearest+neighbour+search%3A+A+survey+and+future+directions%2E+IEEE+Transactions+on+Knowledge+and+Data+Engineering+35%289%29%3A8997%E2%80%939018)
132. Li X, Li P, Hu P (2023c) Revisiting source context in nearest neighbor machine translation. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP) [find](https://scholar.google.com/scholar?q=Li+X%2C+Li+P%2C+Hu+P+%282023c%29+Revisiting+source+context+in+nearest+neighbor+machine+translation%2E+In%3A+Proceedings+of+the+2023+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%29)
133. Li X, Lv K, Yan H, et al (2023d) [Unified demonstration retriever for in-context learning](https://md.research.elamur.ai/free/en/papers/li-2023-unified-demonstration-retriever-context-learning/). In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), pp 4644–4668
134. Li Y, Yang Y, Quan X, et al (2021) Retrieve & memorize: Dialog policy learning with multi-action memory. In: Findings of the Association for Computational Linguistics (ACL/IJCNLP), pp 447–459 [find](https://scholar.google.com/scholar?q=Li+Y%2C+Yang+Y%2C+Quan+X%2C+et+al+%282021%29+Retrieve+%26+memorize%3A+Dialog+policy+learning+with+multi%2Daction+memory%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics+%28ACL%2FIJCNLP%29%2C+pp+447%E2%80%93459)
135. Li Y, Wen H, Wang W, et al (2024a) Personal LLM agents: Insights and survey about the capability, efficiency and security. CoRR abs/2401.05459 [find](https://scholar.google.com/scholar?q=Li+Y%2C+Wen+H%2C+Wang+W%2C+et+al+%282024a%29+Personal+LLM+agents%3A+Insights+and+survey+about+the+capability%2C+efficiency+and+security%2E+CoRR+abs%2F2401%2E05459)
136. Li Y, Yue X, Liao Z, et al (2024b) Attributionbench: How hard is automatic attribution evaluation? In: Findings of the Association for Computational Linguistics: ACL, pp 14919–14935 [find](https://scholar.google.com/scholar?q=Li+Y%2C+Yue+X%2C+Liao+Z%2C+et+al+%282024b%29+Attributionbench%3A+How+hard+is+automatic+attribution+evaluation%3F+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL%2C+pp+14919%E2%80%9314935)
137. Li Z, Guo R, Kumar S (2022b) Decoupled context processing for context augmented language modeling. In: Advances in Neural Information Processing Systems 35 (NeurIPS) [find](https://scholar.google.com/scholar?q=Li+Z%2C+Guo+R%2C+Kumar+S+%282022b%29+Decoupled+context+processing+for+context+augmented+language+modeling%2E+In%3A+Advances+in+Neural+Information+Processing+Systems+35+%28NeurIPS%29)
138. Liang X, Niu S, Li Z, et al (2025) Saferag: Benchmarking security in retrieval-augmented generation of large language model. In: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL), pp 4609–4631 [find](https://scholar.google.com/scholar?q=Liang+X%2C+Niu+S%2C+Li+Z%2C+et+al+%282025%29+Saferag%3A+Benchmarking+security+in+retrieval%2Daugmented+generation+of+large+language+model%2E+In%3A+Proceedings+of+the+63rd+Annual+Meeting+of+the+Association+for+Computati)
139. Lin BY, Tan K, Miller C, et al (2022) Unsupervised cross-task generalization via retrieval augmentation. In: Advances in Neural Information Processing Systems 35 (NeurIPS) [find](https://scholar.google.com/scholar?q=Lin+BY%2C+Tan+K%2C+Miller+C%2C+et+al+%282022%29+Unsupervised+cross%2Dtask+generalization+via+retrieval+augmentation%2E+In%3A+Advances+in+Neural+Information+Processing+Systems+35+%28NeurIPS%29)
140. Liu J (2022) LlamaIndex. https://doi.org/10.5281/zenodo.1234, URL https://github. com/jerryjliu/llama index Liu J, Li L, Xiang T, et al (2023a) TCRA-LLM: token compression retrieval augmented large language model for inference cost reduction. In: Findings of the Association for Computational Linguis… [↗](https://doi.org/10.5281/zenodo.1234)
141. Liu NF, Lin K, Hewitt J, et al (2024) [Lost in the middle: How language models use long contexts](https://md.research.elamur.ai/free/en/papers/liu-2023-lost-middle-language-models-use/). Transactions of the Association for Computational Linguistics 12:157–173
142. Liu S, Wei Y (2015) Fast nearest neighbor searching based on improved vp-tree. Pattern Recognition Letters 60-61:8–15 [find](https://scholar.google.com/scholar?q=Liu+S%2C+Wei+Y+%282015%29+Fast+nearest+neighbor+searching+based+on+improved+vp%2Dtree%2E+Pattern+Recognition+Letters+60%2D61%3A8%E2%80%9315)
143. Liu S, Cho H, Freedman M, et al (2023b) RECAP: retrieval-enhanced context-aware prefix encoder for personalized dialogue response generation. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), pp 8404–8419 [find](https://scholar.google.com/scholar?q=Liu+S%2C+Cho+H%2C+Freedman+M%2C+et+al+%282023b%29+RECAP%3A+retrieval%2Denhanced+context%2Daware+prefix+encoder+for+personalized+dialogue+response+generation%2E+In%3A+Proceedings+of+the+61st+Annual+Meeting+of+the+Associat)
144. Liu Y, Ott M, Goyal N, et al (2019) [Roberta: A robustly optimized BERT pretraining approach](https://md.research.elamur.ai/free/en/papers/liu-2019-roberta-robustly-optimized-bert-pretraining/). CoRR abs/1907.11692
145. Liu Y, Iter D, Xu Y, et al (2023c) [G-eval: NLG evaluation using gpt-4 with better human alignment](https://md.research.elamur.ai/free/en/papers/liu-2023-g-eval-nlg-evaluation-gpt/). In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 2511–2522
146. LMDB (2014) LMDB. Software, URL [https://github.com/LMDB/lmdb](https://github.com/LMDB/lmdb) [↗](https://github.com/LMDB/lmdb)
147. Luo M, Jain S, Gupta A, et al (2023) A study on the efficiency and generalization of light hybrid retrievers. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), pp 1617–1626 [find](https://scholar.google.com/scholar?q=Luo+M%2C+Jain+S%2C+Gupta+A%2C+et+al+%282023%29+A+study+on+the+efficiency+and+generalization+of+light+hybrid+retrievers%2E+In%3A+Proceedings+of+the+61st+Annual+Meeting+of+the+Association+for+Computational+Linguistic)
148. Lyu X, Grafberger S, Biegel S, et al (2023) Improving retrieval-augmented large language models via data importance learning. CoRR abs/2307.03027. https://doi.org/ 10.48550/ARXIV.2307.03027 [find](https://scholar.google.com/scholar?q=Lyu+X%2C+Grafberger+S%2C+Biegel+S%2C+et+al+%282023%29+Improving+retrieval%2Daugmented+large+language+models+via+data+importance+learning%2E+CoRR+abs%2F2307%2E03027%2E+https%3A%2F%2Fdoi%2Eorg%2F+10%2E48550%2FARXIV%2E2307%2E03027)
149. Malkov YA, Yashunin DA (2020) Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence 42(4):824–836 [find](https://scholar.google.com/scholar?q=Malkov+YA%2C+Yashunin+DA+%282020%29+Efficient+and+robust+approximate+nearest+neighbor+search+using+hierarchical+navigable+small+world+graphs%2E+IEEE+Transactions+on+Pattern+Analysis+and+Machine+Intelligence+4)
150. Manakul P, Liusie A, Gales MJF (2023) Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 9004–9017, https://doi.org/10.18653/V1/2023.EMNLP-MAIN.557 [↗](https://doi.org/10.18653/V1/2023.EMNLP-MAIN.557)
151. Mao S, Jiang Y, Chen B, et al (2024) Rafe: Ranking feedback improves query rewriting for RAG. In: Findings of the Association for Computational Linguistics: EMNLP, pp 884–901, https://doi.org/10.18653/V1/2024.FINDINGS-EMNLP.49 [↗](https://doi.org/10.18653/V1/2024.FINDINGS-EMNLP.49)
152. Martinelli I, Ponti MA (2025) Towards secure retrieval-augmented generation: Preventing LLM data leaks with RBAC. In: Proceedings of the 35th Brazilian Conference on Intelligent Systems (BRACIS), Lecture Notes in Computer Science, vol 16182. Springer, pp 500–515 [find](https://scholar.google.com/scholar?q=Martinelli+I%2C+Ponti+MA+%282025%29+Towards+secure+retrieval%2Daugmented+generation%3A+Preventing+LLM+data+leaks+with+RBAC%2E+In%3A+Proceedings+of+the+35th+Brazilian+Conference+on+Intelligent+Systems+%28BRACIS%29%2C+Lect)
153. Martins PH, Marinho Z, Martins AFT (2022) Chunk-based nearest neighbor machine translation. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 4228–4245 [find](https://scholar.google.com/scholar?q=Martins+PH%2C+Marinho+Z%2C+Martins+AFT+%282022%29+Chunk%2Dbased+nearest+neighbor+machine+translation%2E+In%3A+Proceedings+of+the+2022+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%29%2C+pp+4228%E2%80%93)
154. Mei K, Li Z, Xu S, et al (2024) AIOS: LLM agent operating system. CoRR abs/2403.16971 — [arXiv:2403.16971](https://arxiv.org/abs/2403.16971) [find](https://scholar.google.com/scholar?q=Mei+K%2C+Li+Z%2C+Xu+S%2C+et+al+%282024%29+AIOS%3A+LLM+agent+operating+system%2E+CoRR+abs%2F2403%2E16971+%E2%80%94+%5BarXiv%3A2403%2E16971%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2403%2E16971%29)
155. Meng K, Bau D, Andonian A, et al (2022a) Locating and editing factual associations in GPT. In: Advances in Neural Information Processing Systems 35 (NeurIPS) [find](https://scholar.google.com/scholar?q=Meng+K%2C+Bau+D%2C+Andonian+A%2C+et+al+%282022a%29+Locating+and+editing+factual+associations+in+GPT%2E+In%3A+Advances+in+Neural+Information+Processing+Systems+35+%28NeurIPS%29)
156. Meng Y, Li X, Zheng X, et al (2022b) Fast nearest neighbor machine translation. In: Findings of the Association for Computational Linguistics: ACL, pp 555–565 [find](https://scholar.google.com/scholar?q=Meng+Y%2C+Li+X%2C+Zheng+X%2C+et+al+%282022b%29+Fast+nearest+neighbor+machine+translation%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL%2C+pp+555%E2%80%93565)
157. Mesnard T, Hardin C, Dadashi R, et al (2024) Gemma: Open models based on gemini research and technology. CoRR abs/2403.08295 [find](https://scholar.google.com/scholar?q=Mesnard+T%2C+Hardin+C%2C+Dadashi+R%2C+et+al+%282024%29+Gemma%3A+Open+models+based+on+gemini+research+and+technology%2E+CoRR+abs%2F2403%2E08295)
158. Microsoft (2024) Retrieval-augmented generation (rag) applications with autogen. [https://microsoft.github.io/autogen/0.2/blog/2023/10/18/RetrieveChat/](https://microsoft.github.io/autogen/0.2/blog/2023/10/18/RetrieveChat/), accessed: 2026-03-04 [↗](https://microsoft.github.io/autogen/0.2/blog/2023/10/18/RetrieveChat/)
159. Microsoft (2025a) Adding retrieval augmented generation (rag) to semantic kernel agents. [https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/](https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/) agent-rag?pivots=programming-language-csharp, accessed: 2026-03-04 [↗](https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/)
160. Microsoft (2025b) Get started with rag using a prompt flow sample. [https://learn.microsoft.com/en-us/azure/machine-learning/](https://learn.microsoft.com/en-us/azure/machine-learning/) how-to-use-retrieval-augmented-generation?view=azureml-api-2, accessed: 2026-03-04 [↗](https://learn.microsoft.com/en-us/azure/machine-learning/)
161. Microsoft (2025c) Overview of the microsoft 365 copilot retrieval [https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/api/](https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/api/) ai-services/retrieval/overview, accessed: 2026-03-04 api. [↗](https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/api/)
162. Microsoft (2026a) Agentic retrieval in azure ai search. [https://learn.microsoft.com/](https://learn.microsoft.com/) en-us/azure/search/agentic-retrieval-overview, accessed: 2026-03-04 [↗](https://learn.microsoft.com/)
163. Microsoft (2026b) Retrieval-augmented generation (rag) in azure ai search. [https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview](https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview), accessed: 2026-03-04 [↗](https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview)
164. Mihaylov T, Clark P, Khot T, et al (2018) Can a suit of armor conduct electricity? A new dataset for open book question answering. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 2381–2391 [find](https://scholar.google.com/scholar?q=Mihaylov+T%2C+Clark+P%2C+Khot+T%2C+et+al+%282018%29+Can+a+suit+of+armor+conduct+electricity%3F+A+new+dataset+for+open+book+question+answering%2E+In%3A+Proceedings+of+the+2018+Conference+on+Empirical+Methods+in+Natura)
165. Min S, Krishna K, Lyu X, et al (2023a) [Factscore: Fine-grained atomic evaluation of factual precision in long form text generation](https://md.research.elamur.ai/free/en/papers/min-2023-factscore-fine-grained-atomic-evaluation/). In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 12076–12100
166. Min S, Shi W, Lewis M, et al (2023b) Nonparametric masked language modeling. In: Findings of the Association for Computational Linguistics: ACL, pp 2097–2118 [find](https://scholar.google.com/scholar?q=Min+S%2C+Shi+W%2C+Lewis+M%2C+et+al+%282023b%29+Nonparametric+masked+language+modeling%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL%2C+pp+2097%E2%80%932118)
167. Mueller A, Narang K, Mathias L, et al (2023) Meta-training with demonstration retrieval for efficient few-shot learning. In: Findings of the Association for Computational Linguistics: ACL, pp 6049–6064 [find](https://scholar.google.com/scholar?q=Mueller+A%2C+Narang+K%2C+Mathias+L%2C+et+al+%282023%29+Meta%2Dtraining+with+demonstration+retrieval+for+efficient+few%2Dshot+learning%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL%2C+pp+6049%E2%80%93606)
168. Muhamed A, Ribeiro LFR, Dreyer M, et al (2025) [Refusalbench: Generative evaluation of selective refusal in grounded language models](https://md.research.elamur.ai/free/en/papers/muhamed-2026-refusalbench-generative-evaluation-selective-refusal/). CoRR abs/2510.10390
169. Muszynska E (2016) Graph- and surface-level sentence chunking. In: Proceedings of the ACL 2016 Student Research Workshop, pp 93–99 [find](https://scholar.google.com/scholar?q=Muszynska+E+%282016%29+Graph%2D+and+surface%2Dlevel+sentence+chunking%2E+In%3A+Proceedings+of+the+ACL+2016+Student+Research+Workshop%2C+pp+93%E2%80%9399)
170. Narayan S, Cohen SB, Lapata M (2018) [Don’t give me the details, just the summary!](https://md.research.elamur.ai/free/en/papers/narayan-2018-don-t-give-me-details/) topic-aware convolutional neural networks for extreme summarization. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1797–1807
171. Ni B, Liu Z, Wang L, et al (2025) Towards trustworthy retrieval augmented generation for [large language models: A survey](https://md.research.elamur.ai/free/en/papers/minaee-2024-large-language-models-survey/). CoRR abs/2502.06872 — [arXiv:2502.06872](https://arxiv.org/abs/2502.06872)
172. NLTK (2001) NLTK. misc, URL [https://www.nltk.org/](https://www.nltk.org/) [↗](https://www.nltk.org/)
173. OpenAI (2022) [Text-Emb-Ada](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/). URL [https://platform.openai.com/docs/guides/](https://platform.openai.com/docs/guides/) embeddings OpenAI (2023) GPT-4 technical report. CoRR abs/2303.08774 [↗](https://platform.openai.com/docs/guides/)
174. OpenAI (2026) Evals – openai api reference. [https://platform.openai.com/docs/](https://platform.openai.com/docs/) api-reference/evals, accessed: 2026-03-04 [↗](https://platform.openai.com/docs/)
175. Packer C, Fang V, Patil SG, et al (2023) Memgpt: Towards llms as operating systems. CoRR abs/2310.08560 [find](https://scholar.google.com/scholar?q=Packer+C%2C+Fang+V%2C+Patil+SG%2C+et+al+%282023%29+Memgpt%3A+Towards+llms+as+operating+systems%2E+CoRR+abs%2F2310%2E08560)
176. Paranjape B, Lamm M, Tenney I (2022) Retrieval-guided counterfactual generation for QA. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), pp 1670–1686 [find](https://scholar.google.com/scholar?q=Paranjape+B%2C+Lamm+M%2C+Tenney+I+%282022%29+Retrieval%2Dguided+counterfactual+generation+for+QA%2E+In%3A+Proceedings+of+the+60th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28ACL%29%2C+pp+1670%E2%80%931686)
177. Park J, Cho S, Lee J (2025) Stop-rag: Value-based retrieval control for iterative RAG. CoRR abs/2510.14337. https://doi.org/10.48550/ARXIV.2510.14337 — [doi:10.48550/arxiv.2510.14337](https://doi.org/10.48550/arxiv.2510.14337) [↗](https://doi.org/10.48550/ARXIV.2510.14337)
178. Park S, Lee J (2024) Toward robust ralms: Revealing the impact of imperfect retrieval on retrieval-augmented language models. Transactions of the Association for Computational Linguistics 12:1686–1702 [find](https://scholar.google.com/scholar?q=Park+S%2C+Lee+J+%282024%29+Toward+robust+ralms%3A+Revealing+the+impact+of+imperfect+retrieval+on+retrieval%2Daugmented+language+models%2E+Transactions+of+the+Association+for+Computational+Linguistics+12%3A1686%E2%80%931702)
179. Peng X, Qin C, Chen Z, et al (2025) UNIDOC-BENCH: A unified benchmark for document-centric multimodal RAG. CoRR abs/2510.03663 — [arXiv:2510.03663](https://arxiv.org/abs/2510.03663) [find](https://scholar.google.com/scholar?q=Peng+X%2C+Qin+C%2C+Chen+Z%2C+et+al+%282025%29+UNIDOC%2DBENCH%3A+A+unified+benchmark+for+document%2Dcentric+multimodal+RAG%2E+CoRR+abs%2F2510%2E03663+%E2%80%94+%5BarXiv%3A2510%2E03663%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2510%2E03663%29)
180. Petroni F, Rocktäschel T, Riedel S, et al (2019) [Language models as knowledge bases?](https://md.research.elamur.ai/free/en/papers/petroni-2019-language-models-as-knowledge-bases/) In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp 2463–2473
181. Pipitone N, Alami GH (2024) Legalbench-rag: A benchmark for retrieval-augmented generation in the legal domain. CoRR abs/2408.10343 [find](https://scholar.google.com/scholar?q=Pipitone+N%2C+Alami+GH+%282024%29+Legalbench%2Drag%3A+A+benchmark+for+retrieval%2Daugmented+generation+in+the+legal+domain%2E+CoRR+abs%2F2408%2E10343)
182. Radford A, Narasimhan K, Salimans T, et al (2018) Improving language understanding by generative pre-training. OpenAI blog Radford A, Wu J, Child R, et al (2019) Language models are unsupervised multitask learners. OpenAI blog 1(8):9 [find](https://scholar.google.com/scholar?q=Radford+A%2C+Narasimhan+K%2C+Salimans+T%2C+et+al+%282018%29+Improving+language+understanding+by+generative+pre%2Dtraining%2E+OpenAI+blog+Radford+A%2C+Wu+J%2C+Child+R%2C+et+al+%282019%29+Language+models+are+unsupervised+multi)
183. Rajaraman A, Ullman JD (2011) Data Mining, Cambridge University Press, p 1–17 [find](https://scholar.google.com/scholar?q=Rajaraman+A%2C+Ullman+JD+%282011%29+Data+Mining%2C+Cambridge+University+Press%2C+p+1%E2%80%9317)
184. Rajpurkar P, Jia R, Liang P (2018) [Know what you don’t know: Unanswerable questions for squad](https://md.research.elamur.ai/free/en/papers/rajpurkar-2018-know-what-you-don-t/). In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), pp 784–789
185. Ram O, Bezalel L, Zicher A, et al (2023a) What are you token about? dense retrieval as distributions over the vocabulary. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), pp 2481–2498 [find](https://scholar.google.com/scholar?q=Ram+O%2C+Bezalel+L%2C+Zicher+A%2C+et+al+%282023a%29+What+are+you+token+about%3F+dense+retrieval+as+distributions+over+the+vocabulary%2E+In%3A+Proceedings+of+the+61st+Annual+Meeting+of+the+Association+for+Computationa)
186. Ram O, Levine Y, Dalmedigos I, et al (2023b) [In-context retrieval-augmented language models](https://md.research.elamur.ai/free/en/papers/ram-2023-context-retrieval-augmented-language-models/). Transactions of the Association for Computational Linguistics 11:1316– 1331. https://doi.org/10.1162/TACL A 00605 [↗](https://doi.org/10.1162/TACL)
187. Ram P, Sinha K (2019) Revisiting kd-tree for nearest neighbor search. In: Teredesai A, Kumar V, Li Y, et al (eds) Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), pp 1378–1388 [find](https://scholar.google.com/scholar?q=Ram+P%2C+Sinha+K+%282019%29+Revisiting+kd%2Dtree+for+nearest+neighbor+search%2E+In%3A+Teredesai+A%2C+Kumar+V%2C+Li+Y%2C+et+al+%28eds%29+Proceedings+of+the+25th+ACM+SIGKDD+International+Conference+on+Knowledge+Discovery+%26+D)
188. Reid M, Savinov N, Teplyashin D, et al (2024) [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/). CoRR abs/2403.05530
189. Reimers N, Gurevych I (2019) [Sentence-bert: Sentence embeddings using siamese bert-networks](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/). In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp 3980–3990
190. Ren Y, Cao Y, Guo P, et al (2023) Retrieve-and-sample: Document-level event argument extraction via hybrid retrieval augmentation. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), pp 293–306 [find](https://scholar.google.com/scholar?q=Ren+Y%2C+Cao+Y%2C+Guo+P%2C+et+al+%282023%29+Retrieve%2Dand%2Dsample%3A+Document%2Dlevel+event+argument+extraction+via+hybrid+retrieval+augmentation%2E+In%3A+Proceedings+of+the+61st+Annual+Meeting+of+the+Association+for+Com)
191. Robertson SE, Zaragoza H (2009) The probabilistic relevance framework: BM25 and beyond. Foundations and Trends in Information Retrieval 3(4):333–389 [find](https://scholar.google.com/scholar?q=Robertson+SE%2C+Zaragoza+H+%282009%29+The+probabilistic+relevance+framework%3A+BM25+and+beyond%2E+Foundations+and+Trends+in+Information+Retrieval+3%284%29%3A333%E2%80%93389)
192. Roy N, Ribeiro LFR, Blloshmi R, et al (2024) Learning when to retrieve, what to rewrite, and how to respond in conversational QA. In: Findings of the Association for Computational Linguistics: EMNLP, pp 10604–10625, https://doi.org/10.18653/ [find](https://scholar.google.com/scholar?q=Roy+N%2C+Ribeiro+LFR%2C+Blloshmi+R%2C+et+al+%282024%29+Learning+when+to+retrieve%2C+what+to+rewrite%2C+and+how+to+respond+in+conversational+QA%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP%2C+)
193. Ru D, Qiu L, Hu X, et al (2024) Ragchecker: A fine-grained framework for diagnosing retrieval-augmented generation. In: Advances in Neural Information Processing Systems 38 (NeurIPS) [find](https://scholar.google.com/scholar?q=Ru+D%2C+Qiu+L%2C+Hu+X%2C+et+al+%282024%29+Ragchecker%3A+A+fine%2Dgrained+framework+for+diagnosing+retrieval%2Daugmented+generation%2E+In%3A+Advances+in+Neural+Information+Processing+Systems+38+%28NeurIPS%29)
194. Saad-Falcon J, Khattab O, Potts C, et al (2024) ARES: an automated evaluation framework for retrieval-augmented generation systems. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), pp 338–354 [find](https://scholar.google.com/scholar?q=Saad%2DFalcon+J%2C+Khattab+O%2C+Potts+C%2C+et+al+%282024%29+ARES%3A+an+automated+evaluation+framework+for+retrieval%2Daugmented+generation+systems%2E+In%3A+Proceedings+of+the+2024+Conference+of+the+North+American+Chapter)
195. Sachan DS, Reddy S, Hamilton WL, et al (2021) End-to-end training of multi-document reader and retriever for [open-domain question answering](https://md.research.elamur.ai/free/en/papers/chen-2020-open-domain-question-answering/). In: Advances in Neural Information Processing Systems 34 (NeurIPS), pp 25968–25981
196. Sanh V, Debut L, Chaumond J, et al (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108 [find](https://scholar.google.com/scholar?q=Sanh+V%2C+Debut+L%2C+Chaumond+J%2C+et+al+%282019%29+Distilbert%2C+a+distilled+version+of+BERT%3A+smaller%2C+faster%2C+cheaper+and+lighter%2E+CoRR+abs%2F1910%2E01108)
197. Schick T, Dwivedi-Yu J, Dessı̀ R, et al (2023) [Toolformer: Language models can teach themselves to use tools](https://md.research.elamur.ai/free/en/papers/schick-2023-toolformer-language-models-can-teach/). In: Advances in Neural Information Processing Systems 36 (NeurIPS)
198. Sellam T, Das D, Parikh AP (2020) [BLEURT: learning robust metrics for text generation](https://md.research.elamur.ai/free/en/papers/sellam-2020-bleurt-learning-robust-metrics-text/). In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL). Association for Computational Linguistics, pp 7881–7892
199. Shao Z, Gong Y, Shen Y, et al (2023) [Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy](https://md.research.elamur.ai/free/en/papers/shao-2023-enhancing-retrieval-augmented-large-language/). In: Findings of the Association for Computational Linguistics: EMNLP, pp 9248–9274
200. Shen M, Umar M, Maeng K, et al (2024) Towards understanding systems trade-offs in retrieval-augmented generation model inference. CoRR abs/2412.11854 — [arXiv:2412.11854](https://arxiv.org/abs/2412.11854) [find](https://scholar.google.com/scholar?q=Shen+M%2C+Umar+M%2C+Maeng+K%2C+et+al+%282024%29+Towards+understanding+systems+trade%2Doffs+in+retrieval%2Daugmented+generation+model+inference%2E+CoRR+abs%2F2412%2E11854+%E2%80%94+%5BarXiv%3A2412%2E11854%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2412%2E11)
201. Shi W, Min S, Yasunaga M, et al (2024) [REPLUG: retrieval-augmented black-box language models](https://md.research.elamur.ai/free/en/papers/shi-2023-replug-retrieval-augmented-black-box/). In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), pp 8371–8384
202. Shuster K, Poff S, Chen M, et al (2021) [Retrieval augmentation reduces hallucination in conversation](https://md.research.elamur.ai/free/en/papers/shuster-2021-retrieval-augmentation-reduces-hallucination-conversation/). In: Findings of the Association for Computational Linguistics: EMNLP, pp 3784–3803
203. Siino M, Tinnirello I (2024) GPT hallucination detection through prompt engineering. In: Working Notes of the Conference and Labs of the Evaluation Forum (CLEF), pp 712–721 [find](https://scholar.google.com/scholar?q=Siino+M%2C+Tinnirello+I+%282024%29+GPT+hallucination+detection+through+prompt+engineering%2E+In%3A+Working+Notes+of+the+Conference+and+Labs+of+the+Evaluation+Forum+%28CLEF%29%2C+pp+712%E2%80%93721)
204. Siino M, Tinnirello I, Cascia ML (2024) Is text preprocessing still worth the time? A comparative survey on the influence of popular preprocessing methods on transformers and traditional classifiers. Inf Syst 121:102342 [find](https://scholar.google.com/scholar?q=Siino+M%2C+Tinnirello+I%2C+Cascia+ML+%282024%29+Is+text+preprocessing+still+worth+the+time%3F+A+comparative+survey+on+the+influence+of+popular+preprocessing+methods+on+transformers+and+traditional+classifiers%2E+)
205. Siino M, Tinnirello I, Cascia ML (2025) From foundations to GPT in text classification: A comprehensive survey on current approaches and future trends. Found Trends Inf Retr 19(5):557–711 [find](https://scholar.google.com/scholar?q=Siino+M%2C+Tinnirello+I%2C+Cascia+ML+%282025%29+From+foundations+to+GPT+in+text+classification%3A+A+comprehensive+survey+on+current+approaches+and+future+trends%2E+Found+Trends+Inf+Retr+19%285%29%3A557%E2%80%93711)
206. Singhal K, Azizi S, Tu T, et al (2023a) [Large language models encode clinical knowledge](https://md.research.elamur.ai/free/en/papers/singhal-2022-large-language-models-encode-clinical/). Nature 620(7972):172–180
207. Singhal K, Tu T, Gottweis J, et al (2023b) [Towards expert-level medical question answering with large language models](https://md.research.elamur.ai/free/en/papers/singhal-2023-towards-expert-level-medical-question/). CoRR abs/2305.09617
208. Siriwardhana S, Weerasekera R, Kaluarachchi T, et al (2023) [Improving the domain adaptation of retrieval augmented generation (RAG) models for open domain question answering](https://md.research.elamur.ai/free/en/papers/siriwardhana-2023-improving-domain-adaptation-retrieval-augmented/). Transactions of the Association for Computational Linguistics 11:1–17
209. Spotify (2017) Annoy. Software, URL [https://github.com/spotify/annoy](https://github.com/spotify/annoy) [↗](https://github.com/spotify/annoy)
210. Spring (2026) Retrieval augmented generation-spring ai. [https://docs.spring.io/](https://docs.spring.io/) spring-ai/reference/api/retrieval-augmented-generation.html, accessed: 2026-03-04 [↗](https://docs.spring.io/)
211. Su J, Ahmed MHM, Lu Y, et al (2024a) [Roformer: Enhanced transformer with rotary position embedding](https://md.research.elamur.ai/free/en/papers/su-2021-roformer-enhanced-transformer-rotary-position/). Neurocomputing 568:127063
212. Su W, Tang Y, Ai Q, et al (2024b) [DRAGIN: dynamic retrieval augmented generation based on the real-time information needs of large language models](https://md.research.elamur.ai/free/en/papers/su-2024-dragin-dynamic-retrieval-augmented-generation/). In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), pp 12991–13013, https://doi.org/10.18653/V1/2024.ACL-… [↗](https://doi.org/10.18653/V1/2024.ACL-…)
213. Su W, Tang Y, Ai Q, et al (2025a) Parametric retrieval augmented generation. In: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp 1240–1250 [find](https://scholar.google.com/scholar?q=Su+W%2C+Tang+Y%2C+Ai+Q%2C+et+al+%282025a%29+Parametric+retrieval+augmented+generation%2E+In%3A+Proceedings+of+the+48th+International+ACM+SIGIR+Conference+on+Research+and+Development+in+Information+Retrieval+%28SIGIR%29)
214. Su Y, Collier N (2023) Contrastive search is what you need for neural text generation. Transactions on Machine Learning Research 2023 [find](https://scholar.google.com/scholar?q=Su+Y%2C+Collier+N+%282023%29+Contrastive+search+is+what+you+need+for+neural+text+generation%2E+Transactions+on+Machine+Learning+Research+2023)
215. Su Z, Mo F, Nie J (2025b) Parametric retrieval-augmented generation using latent routing of lora adapters. CoRR abs/2511.17044 — [arXiv:2511.17044](https://arxiv.org/abs/2511.17044) [find](https://scholar.google.com/scholar?q=Su+Z%2C+Mo+F%2C+Nie+J+%282025b%29+Parametric+retrieval%2Daugmented+generation+using+latent+routing+of+lora+adapters%2E+CoRR+abs%2F2511%2E17044+%E2%80%94+%5BarXiv%3A2511%2E17044%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2511%2E17044%29)
216. Sulem E, Hay J, Roth D (2021) Do we know what we don’t know? studying unanswerable questions beyond squad 2.0. In: Findings of the Association for Computational Linguistics: EMNLP, pp 4543–4548 [find](https://scholar.google.com/scholar?q=Sulem+E%2C+Hay+J%2C+Roth+D+%282021%29+Do+we+know+what+we+don%E2%80%99t+know%3F+studying+unanswerable+questions+beyond+squad+2%2E0%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP%2C+pp+4543%E2%80%934548)
217. Sun J, Xu C, Tang L, et al (2024) Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph. In: Proceedings of The Twelfth International Conference on Learning Representations (ICLR) [find](https://scholar.google.com/scholar?q=Sun+J%2C+Xu+C%2C+Tang+L%2C+et+al+%282024%29+Think%2Don%2Dgraph%3A+Deep+and+responsible+reasoning+of+large+language+model+on+knowledge+graph%2E+In%3A+Proceedings+of+The+Twelfth+International+Conference+on+Learning+Represe)
218. Tan Y, He S, Liao H, et al (2025) Dynamic parametric retrieval augmented generation for test-time knowledge enhancement. CoRR abs/2503.23895 — [arXiv:2503.23895](https://arxiv.org/abs/2503.23895) [find](https://scholar.google.com/scholar?q=Tan+Y%2C+He+S%2C+Liao+H%2C+et+al+%282025%29+Dynamic+parametric+retrieval+augmented+generation+for+test%2Dtime+knowledge+enhancement%2E+CoRR+abs%2F2503%2E23895+%E2%80%94+%5BarXiv%3A2503%2E23895%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2503%2E23895%29)
219. Team L (2024) [The llama 3 herd of models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/). CoRR abs/2407.21783
220. Thakur N, Reimers N, Rücklé A, et al (2021) BEIR: A heterogeneous benchmark for zero-shot evaluation of information retrieval models. In: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks) [find](https://scholar.google.com/scholar?q=Thakur+N%2C+Reimers+N%2C+Ru%CC%88ckle%CC%81+A%2C+et+al+%282021%29+BEIR%3A+A+heterogeneous+benchmark+for+zero%2Dshot+evaluation+of+information+retrieval+models%2E+In%3A+Proceedings+of+the+Neural+Information+Processing+Systems+Tra)
221. Touvron H, Lavril T, Izacard G, et al (2023a) [Llama: Open and efficient foundation language models](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-open-efficient-foundation-language/). CoRR abs/2302.13971
222. Touvron H, Martin L, Stone K, et al (2023b) [Llama 2: Open foundation and fine-tuned chat models](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-2-open-foundation-fine/). CoRR abs/2307.09288
223. Uddin MN, Saeidi A, Handa D, et al (2025) [Unseentimeqa: Time-sensitive question-answering beyond llms’ memorization](https://md.research.elamur.ai/free/en/papers/uddin-2025-unseentimeqa-time-sensitive-question-answering/). In: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL), pp 1873–1913
224. Vaithilingam P, Zhang T, Glassman EL (2022) Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Barbosa SDJ, Lampe C, Appert C, et al (eds) CHI Conference on Human Factors in Computing Systems (CHI), pp 332:1–332:7 [find](https://scholar.google.com/scholar?q=Vaithilingam+P%2C+Zhang+T%2C+Glassman+EL+%282022%29+Expectation+vs%2E+experience%3A+Evaluating+the+usability+of+code+generation+tools+powered+by+large+language+models%2E+In%3A+Barbosa+SDJ%2C+Lampe+C%2C+Appert+C%2C+et+al+%28e)
225. Vaswani A, Shazeer N, Parmar N, et al (2017) [Attention is all you need](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/). In: Advances in Neural Information Processing Systems 30 (NeurIPS), pp 5998–6008
226. Vercel (2026) Rag agent guide – vercel ai sdk. [https://ai-sdk.dev/cookbook/guides/](https://ai-sdk.dev/cookbook/guides/) rag-chatbot, accessed: 2026-03-04 [↗](https://ai-sdk.dev/cookbook/guides/)
227. Vu T, Iyyer M, Wang X, et al (2024) [Freshllms: Refreshing large language models with search engine augmentation](https://md.research.elamur.ai/free/en/papers/vu-2023-freshllms-refreshing-large-language-models/). In: Findings of the Association for Computational Linguistics: ACL, pp 13697–13720, https://doi.org/10.18653/V1/2024. FINDINGS-ACL.813 [↗](https://doi.org/10.18653/V1/2024)
228. Wang B, Ping W, Xu P, et al (2023a) Shall we pretrain autoregressive language models with retrieval? A comprehensive study. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 7763–7786 [find](https://scholar.google.com/scholar?q=Wang+B%2C+Ping+W%2C+Xu+P%2C+et+al+%282023a%29+Shall+we+pretrain+autoregressive+language+models+with+retrieval%3F+A+comprehensive+study%2E+In%3A+Proceedings+of+the+2023+Conference+on+Empirical+Methods+in+Natural+Langu)
229. Wang B, Ping W, McAfee L, et al (2024a) Instructretro: Instruction tuning post retrieval-augmented pretraining. In: Proceedings of the Forty-first International Conference on Machine Learning (ICML), pp 51255–51272 [find](https://scholar.google.com/scholar?q=Wang+B%2C+Ping+W%2C+McAfee+L%2C+et+al+%282024a%29+Instructretro%3A+Instruction+tuning+post+retrieval%2Daugmented+pretraining%2E+In%3A+Proceedings+of+the+Forty%2Dfirst+International+Conference+on+Machine+Learning+%28ICML%29%2C+)
230. Wang J, Yi X, Guo R, et al (2021a) Milvus: A purpose-built vector data management system. In: SIGMOD ’21: International Conference on Management of Data. ACM, pp 2614–2627 [find](https://scholar.google.com/scholar?q=Wang+J%2C+Yi+X%2C+Guo+R%2C+et+al+%282021a%29+Milvus%3A+A+purpose%2Dbuilt+vector+data+management+system%2E+In%3A+SIGMOD+%E2%80%9921%3A+International+Conference+on+Management+of+Data%2E+ACM%2C+pp+2614%E2%80%932627)
231. Wang L, Ma C, Feng X, et al (2024b) [A survey on large language model based autonomous agents](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-large-language-model-based/). Frontiers in Computer Science 18(6):186345
232. Wang S, Xu Y, Fang Y, et al (2022) Training data is more valuable than you think: A simple and effective method by retrieving from training data. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), pp 3170–3179 [find](https://scholar.google.com/scholar?q=Wang+S%2C+Xu+Y%2C+Fang+Y%2C+et+al+%282022%29+Training+data+is+more+valuable+than+you+think%3A+A+simple+and+effective+method+by+retrieving+from+training+data%2E+In%3A+Proceedings+of+the+60th+Annual+Meeting+of+the+Asso)
233. Wang S, Zhu Y, Liu H, et al (2025a) Knowledge editing for [large language models: A survey](https://md.research.elamur.ai/free/en/papers/minaee-2024-large-language-models-survey/). ACM Computing Surveys 57(3):59:1–59:37
234. Wang W, Dong L, Cheng H, et al (2023b) Augmenting language models with long-term memory. In: Advances in Neural Information Processing Systems 36 (NeurIPS) [find](https://scholar.google.com/scholar?q=Wang+W%2C+Dong+L%2C+Cheng+H%2C+et+al+%282023b%29+Augmenting+language+models+with+long%2Dterm+memory%2E+In%3A+Advances+in+Neural+Information+Processing+Systems+36+%28NeurIPS%29)
235. Wang W, Zou W, Min J (2025b) Solving LLM repetition problem in production: A comprehensive study of multiple solutions. CoRR abs/2512.04419. https://doi.org/ 10.48550/ARXIV.2512.04419 — [doi:10.48550/arxiv.2512.04419](https://doi.org/10.48550/arxiv.2512.04419) [find](https://scholar.google.com/scholar?q=Wang+W%2C+Zou+W%2C+Min+J+%282025b%29+Solving+LLM+repetition+problem+in+production%3A+A+comprehensive+study+of+multiple+solutions%2E+CoRR+abs%2F2512%2E04419%2E+https%3A%2F%2Fdoi%2Eorg%2F+10%2E48550%2FARXIV%2E2512%2E04419+%E2%80%94+%5Bdoi%3A10%2E48550%2F)
236. Wang X, Jiang Y, Bach N, et al (2021b) [Improving named entity recognition by external context retrieving and cooperative learning](https://md.research.elamur.ai/free/en/papers/wang-2021-improving-named-entity-recognition-external/). In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (A…
237. Wang Y, Li P, Sun M, et al (2023c) [Self-knowledge guided retrieval augmentation for large language models](https://md.research.elamur.ai/free/en/papers/wang-2023-self-knowledge-guided-retrieval-augmentation/). In: Findings of the Association for Computational Linguistics: EMNLP, pp 10303–10315
238. Wang Z, Araki J, Jiang Z, et al (2023d) [Learning to filter context for retrieval-augmented generation](https://md.research.elamur.ai/free/en/papers/wang-2023-learning-filter-context-retrieval-augmented/). CoRR abs/2311.08377. https://doi.org/10.48550/ARXIV. 2311.08377 [↗](https://doi.org/10.48550/ARXIV)
239. Wang Z, Nie W, Qiao Z, et al (2023e) Retrieval-based controllable molecule generation. In: The Eleventh International Conference on Learning Representations, (ICLR) [find](https://scholar.google.com/scholar?q=Wang+Z%2C+Nie+W%2C+Qiao+Z%2C+et+al+%282023e%29+Retrieval%2Dbased+controllable+molecule+generation%2E+In%3A+The+Eleventh+International+Conference+on+Learning+Representations%2C+%28ICLR%29)
240. Wornow M, Lozano A, Dash D, et al (2024) Zero-shot clinical trial patient matching with llms. CoRR abs/2402.05125 — [arXiv:2402.05125](https://arxiv.org/abs/2402.05125) [find](https://scholar.google.com/scholar?q=Wornow+M%2C+Lozano+A%2C+Dash+D%2C+et+al+%282024%29+Zero%2Dshot+clinical+trial+patient+matching+with+llms%2E+CoRR+abs%2F2402%2E05125+%E2%80%94+%5BarXiv%3A2402%2E05125%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2402%2E05125%29)
241. Wu M, Goodman ND (2018) Multimodal generative models for scalable weakly-supervised learning. In: Advances in Neural Information Processing Systems 31 (NeurIPS), pp 5580–5590 [find](https://scholar.google.com/scholar?q=Wu+M%2C+Goodman+ND+%282018%29+Multimodal+generative+models+for+scalable+weakly%2Dsupervised+learning%2E+In%3A+Advances+in+Neural+Information+Processing+Systems+31+%28NeurIPS%29%2C+pp+5580%E2%80%935590)
242. Wu S, Xiong Y, Cui Y, et al (2024) Refusion: Improving natural language understanding with computation-efficient retrieval representation fusion. In: Proceedings of The Twelfth International Conference on Learning Representations (ICLR) [find](https://scholar.google.com/scholar?q=Wu+S%2C+Xiong+Y%2C+Cui+Y%2C+et+al+%282024%29+Refusion%3A+Improving+natural+language+understanding+with+computation%2Defficient+retrieval+representation+fusion%2E+In%3A+Proceedings+of+The+Twelfth+International+Conferenc)
243. Wu Y, Rabe MN, Hutchins D, et al (2022) Memorizing transformers. In: Proceedings of The Tenth International Conference on Learning Representations (ICLR) [find](https://scholar.google.com/scholar?q=Wu+Y%2C+Rabe+MN%2C+Hutchins+D%2C+et+al+%282022%29+Memorizing+transformers%2E+In%3A+Proceedings+of+The+Tenth+International+Conference+on+Learning+Representations+%28ICLR%29)
244. Xi Z, Chen W, Guo X, et al (2025) [The rise and potential of large language model based agents: a survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/). Science China Information Sciences 68(2)
245. Xia S, Wang X, Liang J, et al (2025) Ground every sentence: Improving retrieval-augmented llms with interleaved reference-claim generation. In: Findings of the Association for Computational Linguistics (NAACL), pp 969–988 [find](https://scholar.google.com/scholar?q=Xia+S%2C+Wang+X%2C+Liang+J%2C+et+al+%282025%29+Ground+every+sentence%3A+Improving+retrieval%2Daugmented+llms+with+interleaved+reference%2Dclaim+generation%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistic)
246. Xiang Z, Wu C, Zhang Q, et al (2025) When to use graphs in RAG: A comprehensive analysis for graph retrieval-augmented generation. CoRR abs/2506.05690 — [arXiv:2506.05690](https://arxiv.org/abs/2506.05690) [find](https://scholar.google.com/scholar?q=Xiang+Z%2C+Wu+C%2C+Zhang+Q%2C+et+al+%282025%29+When+to+use+graphs+in+RAG%3A+A+comprehensive+analysis+for+graph+retrieval%2Daugmented+generation%2E+CoRR+abs%2F2506%2E05690+%E2%80%94+%5BarXiv%3A2506%2E05690%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2506%2E0)
247. Xiao S, Liu Z, Zhang P, et al (2023) [C-pack: Packaged resources to advance general chinese embedding](https://md.research.elamur.ai/free/en/papers/xiao-2023-c-pack-packed-resources/). CoRR abs/2309.07597
248. Xie J, Chen Z, Zhang R, et al (2024) Large multimodal agents: A survey. CoRR abs/2402.15116 — [arXiv:2402.15116](https://arxiv.org/abs/2402.15116) [find](https://scholar.google.com/scholar?q=Xie+J%2C+Chen+Z%2C+Zhang+R%2C+et+al+%282024%29+Large+multimodal+agents%3A+A+survey%2E+CoRR+abs%2F2402%2E15116+%E2%80%94+%5BarXiv%3A2402%2E15116%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2402%2E15116%29)
249. Xin J, Tang R, Yu Y, et al (2021) The art of abstention: Selective prediction and error regularization for natural language processing. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processi… [find](https://scholar.google.com/scholar?q=Xin+J%2C+Tang+R%2C+Yu+Y%2C+et+al+%282021%29+The+art+of+abstention%3A+Selective+prediction+and+error+regularization+for+natural+language+processing%2E+In%3A+Proceedings+of+the+59th+Annual+Meeting+of+the+Association+fo)
250. Xiong G, Jin Q, Lu Z, et al (2024) [Benchmarking retrieval-augmented generation for medicine](https://md.research.elamur.ai/free/en/papers/xiong-2024-benchmarking-retrieval-augmented-generation-medicine/). In: Findings of the Association for Computational Linguistics: ACL, pp 6233–6251
251. Xiong Y, Yang X, Liu L, et al (2023) EARA: improving biomedical semantic textual similarity with entity-aligned attention and retrieval augmentation. In: Findings of the Association for Computational Linguistics: EMNLP, pp 8760–8771 [find](https://scholar.google.com/scholar?q=Xiong+Y%2C+Yang+X%2C+Liu+L%2C+et+al+%282023%29+EARA%3A+improving+biomedical+semantic+textual+similarity+with+entity%2Daligned+attention+and+retrieval+augmentation%2E+In%3A+Findings+of+the+Association+for+Computational+)
252. Xu F, Shi W, Choi E (2024) RECOMP: improving retrieval-augmented lms with context compression and selective augmentation. In: Proceedings of The Twelfth International Conference on Learning Representations (ICLR) [find](https://scholar.google.com/scholar?q=Xu+F%2C+Shi+W%2C+Choi+E+%282024%29+RECOMP%3A+improving+retrieval%2Daugmented+lms+with+context+compression+and+selective+augmentation%2E+In%3A+Proceedings+of+The+Twelfth+International+Conference+on+Learning+Representa)
253. Xu FF, Alon U, Neubig G (2023) Why do nearest neighbor language models work? In: Proceedings of the 40th International Conference on Machine Learning (ICML), pp 38325–38341 [find](https://scholar.google.com/scholar?q=Xu+FF%2C+Alon+U%2C+Neubig+G+%282023%29+Why+do+nearest+neighbor+language+models+work%3F+In%3A+Proceedings+of+the+40th+International+Conference+on+Machine+Learning+%28ICML%29%2C+pp+38325%E2%80%9338341)
254. Yan S, Gu J, Zhu Y, et al (2024) [Corrective retrieval augmented generation](https://md.research.elamur.ai/free/en/papers/yan-2024-corrective-retrieval-augmented-generation/). CoRR abs/2401.15884
255. Yang A, Yang B, Hui B, et al (2024a) Qwen2 technical report. CoRR abs/2407.10671 [find](https://scholar.google.com/scholar?q=Yang+A%2C+Yang+B%2C+Hui+B%2C+et+al+%282024a%29+Qwen2+technical+report%2E+CoRR+abs%2F2407%2E10671)
256. Yang A, Yang B, Zhang B, et al (2024b) Qwen2.5 technical report. CoRR abs/2412.15115 [find](https://scholar.google.com/scholar?q=Yang+A%2C+Yang+B%2C+Zhang+B%2C+et+al+%282024b%29+Qwen2%2E5+technical+report%2E+CoRR+abs%2F2412%2E15115)
257. Yang Z, Qi P, Zhang S, et al (2018) [Hotpotqa: A dataset for diverse, explainable multi-hop question answering](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/). In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 2369–2380
258. Yu G, Liu L, Jiang H, et al (2023a) Retrieval-augmented few-shot text classification. In: Findings of the Association for Computational Linguistics: EMNLP, pp 6721–6735 [find](https://scholar.google.com/scholar?q=Yu+G%2C+Liu+L%2C+Jiang+H%2C+et+al+%282023a%29+Retrieval%2Daugmented+few%2Dshot+text+classification%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP%2C+pp+6721%E2%80%936735)
259. Yu H, Gan A, Zhang K, et al (2024) Evaluation of retrieval-augmented generation: A survey. CoRR abs/2405.07437 — [arXiv:2405.07437](https://arxiv.org/abs/2405.07437) [find](https://scholar.google.com/scholar?q=Yu+H%2C+Gan+A%2C+Zhang+K%2C+et+al+%282024%29+Evaluation+of+retrieval%2Daugmented+generation%3A+A+survey%2E+CoRR+abs%2F2405%2E07437+%E2%80%94+%5BarXiv%3A2405%2E07437%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2405%2E07437%29)
260. Yu X, Jian P, Chen C (2025) Tablerag: A retrieval augmented generation framework for heterogeneous document reasoning. In: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 14063–14082 [find](https://scholar.google.com/scholar?q=Yu+X%2C+Jian+P%2C+Chen+C+%282025%29+Tablerag%3A+A+retrieval+augmented+generation+framework+for+heterogeneous+document+reasoning%2E+In%3A+Proceedings+of+the+2025+Conference+on+Empirical+Methods+in+Natural+Language+P)
261. Yu Z, Xiong C, Yu S, et al (2023b) Augmentation-adapted retriever improves generalization of language models as generic plug-in. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), pp 2421–2436 [find](https://scholar.google.com/scholar?q=Yu+Z%2C+Xiong+C%2C+Yu+S%2C+et+al+%282023b%29+Augmentation%2Dadapted+retriever+improves+generalization+of+language+models+as+generic+plug%2Din%2E+In%3A+Proceedings+of+the+61st+Annual+Meeting+of+the+Association+for+Compu)
262. Yue X, Wang B, Chen Z, et al (2023) Automatic evaluation of attribution by large language models. In: Findings of the Association for Computational Linguistics: EMNLP, pp 4615–4635 [find](https://scholar.google.com/scholar?q=Yue+X%2C+Wang+B%2C+Chen+Z%2C+et+al+%282023%29+Automatic+evaluation+of+attribution+by+large+language+models%2E+In%3A+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP%2C+pp+4615%E2%80%934635)
263. Zeng A, Liu X, Du Z, et al (2023) [GLM-130B: an open bilingual pre-trained model](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/). In: The Eleventh International Conference on Learning Representations (ICLR).
264. OpenReview.net Zeng S, Zhang J, He P, et al (2025) Mitigating the privacy issues in retrieval-augmented generation (RAG) via pure synthetic data. In: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 24527–24558 [find](https://scholar.google.com/scholar?q=OpenReview%2Enet+Zeng+S%2C+Zhang+J%2C+He+P%2C+et+al+%282025%29+Mitigating+the+privacy+issues+in+retrieval%2Daugmented+generation+%28RAG%29+via+pure+synthetic+data%2E+In%3A+Proceedings+of+the+2025+Conference+on+Empirical+Me)
265. Zhang B, Sennrich R (2019) Root mean square layer normalization. In: Advances in Neural Information Processing Systems 32 (NeurIPS), pp 12360–12371 [find](https://scholar.google.com/scholar?q=Zhang+B%2C+Sennrich+R+%282019%29+Root+mean+square+layer+normalization%2E+In%3A+Advances+in+Neural+Information+Processing+Systems+32+%28NeurIPS%29%2C+pp+12360%E2%80%9312371)
266. Zhang H, Chen J, Jiang F, et al (2023a) [Huatuogpt, towards taming language model to be a doctor](https://md.research.elamur.ai/free/en/papers/zhang-2023-huatuogpt-taming-language-model-be/). In: Findings of the Association for Computational Linguistics: EMNLP, pp 10859–10885
267. Zhang J, Muhamed A, Anantharaman A, et al (2023b) Reaugkd: Retrieval-augmented knowledge distillation for pre-trained language models. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), pp 1128– 1136 [find](https://scholar.google.com/scholar?q=Zhang+J%2C+Muhamed+A%2C+Anantharaman+A%2C+et+al+%282023b%29+Reaugkd%3A+Retrieval%2Daugmented+knowledge+distillation+for+pre%2Dtrained+language+models%2E+In%3A+Proceedings+of+the+61st+Annual+Meeting+of+the+Association+for)
268. Zhang N, Yao Y, Tian B, et al (2024) A comprehensive study of knowledge editing for large language models. CoRR abs/2401.01286 [find](https://scholar.google.com/scholar?q=Zhang+N%2C+Yao+Y%2C+Tian+B%2C+et+al+%282024%29+A+comprehensive+study+of+knowledge+editing+for+large+language+models%2E+CoRR+abs%2F2401%2E01286)
269. Zhang Z, Dai Q, Bo X, et al (2025) A survey on the memory mechanism of large language model-based agents. ACM Transactions on Information Systems 43(6):155:1–155:47 [find](https://scholar.google.com/scholar?q=Zhang+Z%2C+Dai+Q%2C+Bo+X%2C+et+al+%282025%29+A+survey+on+the+memory+mechanism+of+large+language+model%2Dbased+agents%2E+ACM+Transactions+on+Information+Systems+43%286%29%3A155%3A1%E2%80%93155%3A47)
270. Zhao P, Zhang H, Yu Q, et al (2024) [Retrieval-augmented generation for ai-generated content: A survey](https://md.research.elamur.ai/free/en/papers/zhao-2024-retrieval-augmented-generation-ai-generated/). CoRR abs/2402.19473
271. Zheng L, Chiang W, Sheng Y, et al (2023) [Judging llm-as-a-judge with mt-bench and chatbot arena](https://md.research.elamur.ai/free/en/papers/zheng-2023-judging-llm-judge-mt-bench/). In: Advances in Neural Information Processing Systems 36 (NeurIPS)
272. Zheng X, Zhang Z, Guo J, et al (2021) [Adaptive nearest neighbor machine translation](https://md.research.elamur.ai/free/en/papers/zheng-2021-adaptive-nearest-neighbor-machine-translation/). In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL/IJCNLP), pp 368–374, https://doi.org/10.186…
273. Zhong Z, Lei T, Chen D (2022) Training language models with memory augmentation. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 5657–5673, https://doi.org/10.18653/V1/2022. EMNLP-MAIN.382 [↗](https://doi.org/10.18653/V1/2022)
274. Zhu R, Liu X, Sun Z, et al (2025) [Mitigating lost-in-retrieval problems in retrieval augmented multi-hop question answering](https://md.research.elamur.ai/free/en/papers/zhu-2025-mitigating-lost-retrieval-problems-retrieval/). In: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL), pp 22362–22375
275. Zhu W, Xu J, Huang S, et al (2023) INK: injecting knn knowledge in nearest neighbor machine translation. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), pp 15948–15959 [find](https://scholar.google.com/scholar?q=Zhu+W%2C+Xu+J%2C+Huang+S%2C+et+al+%282023%29+INK%3A+injecting+knn+knowledge+in+nearest+neighbor+machine+translation%2E+In%3A+Proceedings+of+the+61st+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28AC)
276. Zou W, Geng R, Wang B, et al (2025) Poisonedrag: Knowledge corruption attacks to retrieval-augmented generation of large language models. In: Proceedings of the 34th USENIX Security Symposium (USENIX Security), pp 3827–3844 [find](https://scholar.google.com/scholar?q=Zou+W%2C+Geng+R%2C+Wang+B%2C+et+al+%282025%29+Poisonedrag%3A+Knowledge+corruption+attacks+to+retrieval%2Daugmented+generation+of+large+language+models%2E+In%3A+Proceedings+of+the+34th+USENIX+Security+Symposium+%28USENIX+)

*Updated 1 November 2026*