---
title: "In-Context Retrieval-Augmented Language Models — Ram 2023"
description: "The authors from AI21 Labs propose In-Context RALM: the retrieved passage is simply prepended… 49 references, cited by 358 papers here."
authors: ["Ori Ram", "Yoav Levine", "Itay Dalmedigos", "Dor Muhlgay", "Amnon Shashua", "Kevin Leyton-Brown", "Yoav Shoham"]
year: 2023
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/2023.tacl-1.75/"
doi: "10.1162/tacl_a_00605"
arxiv: "2302.00083"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ram-2023-context-retrieval-augmented-language-models/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ram-2023-context-retrieval-augmented-language-models/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2023](https://md.research.elamur.ai/free/en/papers/2023/) / In-Context Retrieval-Augmented Language Models

# In-Context Retrieval-Augmented Language Models

**Authors:** [Ori Ram](https://md.research.elamur.ai/free/en/authors/ori-ram/), [Yoav Levine](https://md.research.elamur.ai/free/en/authors/yoav-levine/), [Itay Dalmedigos](https://md.research.elamur.ai/free/en/authors/itay-dalmedigos/), [Dor Muhlgay](https://md.research.elamur.ai/free/en/authors/dor-muhlgay/), [Amnon Shashua](https://md.research.elamur.ai/free/en/authors/amnon-shashua/), [Kevin Leyton-Brown](https://md.research.elamur.ai/free/en/authors/kevin-leyton-brown/) and [Yoav Shoham](https://md.research.elamur.ai/free/en/authors/yoav-shoham/) · **Year:** 2023 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2023](https://md.research.elamur.ai/free/en/venues/tacl/2023/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.tacl-1.75/) · [PDF](https://aclanthology.org/2023.tacl-1.75.pdf) · [DOI 10.1162/tacl\_a\_00605](https://doi.org/10.1162/tacl_a_00605) · [arXiv 2302.00083](https://arxiv.org/abs/2302.00083) · [OpenAlex](https://openalex.org/W4388778348)

**Citations:** 435 citations (OpenAlex, 2026-10-14) · [358](https://md.research.elamur.ai/free/en/papers/ram-2023-context-retrieval-augmented-language-models/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Language models often fabricate facts and do not indicate where they obtained their information. Retrieval-augmented approaches that draw on external documents (RALM) mitigate this problem. However, known solutions such as RETRO modify the model architecture and require additional training, so they are rarely used in practice. The authors from AI21 Labs propose In-Context RALM: the retrieved passage is simply prepended to the input text, while the model remains frozen and entirely unchanged. The approach was evaluated on five corpora and on models ranging from 110M to 66B parameters. The authors also separately investigated how often retrieval should be performed and how many of the most recent tokens should be used for the query. In addition, they proposed two methods for reranking candidates: zero-shot reranking using a small LM, and a trained bidirectional reranker that receives a signal from the LM itself. Off-the-shelf BM25 alone yields a gain comparable to a 2–3× increase in model size, and reranking adds roughly another twofold effect. The method is compatible even with models accessible only through an API. Grounding generation in sources can therefore be achieved cheaply, including for non-standard domains and private data.

## Key points
- Corpora: WikiText-103, RealNews, as well as ArXiv, Stack Exchange and FreeLaw from The Pile. Models: GPT-2, GPT-Neo, OPT, LLaMA (from 110M to 66B parameters).
- GPT-2 345M with BM25 outperforms GPT-2 762M, and with the trained reranker it outperforms GPT-2 1.5B. OPT 6.7B with an off-the-shelf retriever reaches the level of OPT 66B.
- A predictive reranker trained on WikiText-103 with data from GPT-2 110M reduces perplexity: for GPT-2 110M from 29.6 to 26.8, and for GPT-2 1.5B from 16.6 to 15.4.
- Setting the query length equal to the retrieval stride (as in RETRO) degrades quality. These two parameters are better tuned separately.
- In zero-shot ODQA with DPR and two documents, LLaMA-13B improves exact match on Natural Questions from 12.0 to 31.0, and on TriviaQA from 54.8 to 60.1. The code is publicly available: github.com/AI21Labs/in-context-ralm (TACL, vol. 11, 2023).

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

📚 Shelves (5): [🖥️ 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 · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,437 works

🧭 Topics (2): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,320 works · [🔍 Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · 473 works

💡 Concepts (1): [💡 Retrieval-augmented generation](https://md.research.elamur.ai/free/en/concepts/retrieval-augmented-generation/) · 50 works

## Cite

Cite as: Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown and Yoav Shoham. 2023. In-Context Retrieval-Augmented Language Models. Transactions of the Association for Computational Linguistics.

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

```bibtex
@article{ram2023context,
  title = {{In-Context Retrieval-Augmented Language Models}},
  author = {Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown and Yoav Shoham},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2023},
  doi = {10.1162/tacl\_a\_00605},
  eprint = {2302.00083},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1162/tacl_a_00605}
}
```

- [← previous Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/)
- [next → Can Large Language Models Transform Computational Social Science?](https://md.research.elamur.ai/free/en/papers/ziems-2023-large-language-models-transform-computational/)
- [↑ back to papers of 2023](https://md.research.elamur.ai/free/en/papers/2023/)

## References

49 references, 27 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Uri Alon, Frank Xu, Junxian He, Sudipta Sengupta, Dan Roth, and Graham Neubig. 2022. Neuro-symbolic language modeling with automaton-augmented retrieval. In ICML. [find](https://scholar.google.com/scholar?q=Uri+Alon%2C+Frank+Xu%2C+Junxian+He%2C+Sudipta+Sengupta%2C+Dan+Roth%2C+and+Graham+Neubig%2E+2022%2E+Neuro%2Dsymbolic+language+modeling+with+automaton%2Daugmented+retrieval%2E+In+ICML%2E)
2. Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman. 2021. GPT-Neo: Large scale autoregressive language modeling with mesh-tensorflow. https://doi.org/10.5281/zenodo.5297715 — [doi:10.5281/zenodo.5297715](https://doi.org/10.5281/zenodo.5297715) [↗](https://doi.org/10.5281/zenodo.5297715)
3. Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego De Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin… [find](https://scholar.google.com/scholar?q=Sebastian+Borgeaud%2C+Arthur+Mensch%2C+Jordan+Hoffmann%2C+Trevor+Cai%2C+Eliza+Rutherford%2C+Katie+Millican%2C+George+Bm+Van+Den+Driessche%2C+Jean%2DBaptiste+Lespiau%2C+Bogdan+Damoc%2C+Aidan+Clark%2C+Diego+De+Las+Casas%2C+Aur)
4. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chr… — [Reading Wikipedia to Answer Open-Domain Questions](https://md.research.elamur.ai/free/en/papers/chen-2017-reading-wikipedia-answer-open-domain/)
5. We would like to thank the reviewers and the Action Editor for their valuable feedback. [find](https://scholar.google.com/scholar?q=We+would+like+to+thank+the+reviewers+and+the+Action+Editor+for+their+valuable+feedback%2E)
6. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 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,…
7. Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, Lei Li, and Zhifang Sui. 2023. [A survey on in-context learning](https://md.research.elamur.ai/free/en/papers/dong-2023-survey-context-learning/). https://doi.org/10 .48550/arXiv.2301.00234
8. Martin Fajcik, Martin Docekal, Karel Ondrej, and Pavel Smrz. 2021. R2-D2: A modular baseline for [open-domain question answering](https://md.research.elamur.ai/free/en/papers/chen-2020-open-domain-question-answering/). In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 854–870, Punta Cana, Dominican Republic. Association for Computational Linguistics. https:/…
9. Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy. 2021. The pile: An 800gb dataset of diverse text for language modeling. https://doi.org/10.48550/arXiv.2101.00027 [↗](https://doi.org/10.48550/arXiv.2101.00027)
10. Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020. [REALM: Retrieval-augmented language model pre-training](https://md.research.elamur.ai/free/en/papers/guu-2020-realm-retrieval-augmented-language-model/). In ICML.
11. Junxian He, Graham Neubig, and Taylor Berg-Kirkpatrick. 2021. [Efficient nearest neighbor language models](https://md.research.elamur.ai/free/en/papers/he-2021-efficient-nearest-neighbor-language-models/). In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5703–5714, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.…
12. Minlie Huang, Xiaoyan Zhu, and Jianfeng Gao. 2020. Challenges in building intelligent open-domain dialog systems. ACM Transactions on Information Systems, 38(3). https://doi.org/10.1145/3383123 [↗](https://doi.org/10.1145/3383123)
13. Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2022a. [Unsupervised dense information retrieval with contrastive learning](https://md.research.elamur.ai/free/en/papers/izacard-2021-unsupervised-dense-information-retrieval-contrastive/). Transactions on Machine Learning Research.
14. Gautier Izacard and Edouard Grave. 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: Main Volume, pages 874–880, Online. Association for Computa…
15. Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2022b. [Atlas: Few-shot learning with retrieval augmented language models](https://md.research.elamur.ai/free/en/papers/izacard-2022-atlas-few-shot-learning-retrieval/). https://doi.org/10 .48550/arXiv.2208.03299
16. Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2021. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, 7(3):535–547. https://doi.org/10.1109/TBDATA.2019.2921572 — [doi:10.1109/tbdata.2019.2921572](https://doi.org/10.1109/tbdata.2019.2921572) [↗](https://doi.org/10.1109/TBDATA.2019.2921572)
17. Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017. [TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension](https://md.research.elamur.ai/free/en/papers/joshi-2017-triviaqa-large-scale-distantly-supervised/). In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1601–1611, Vancouver…
18. Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 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), pages 6769–6781, Onli…
19. Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2020. [find](https://scholar.google.com/scholar?q=Urvashi+Khandelwal%2C+Omer+Levy%2C+Dan+Jurafsky%2C+Luke+Zettlemoyer%2C+and+Mike+Lewis%2E+2020%2E)
20. Generalization through memorization: Nearest neighbor language models. In International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Generalization+through+memorization%3A+Nearest+neighbor+language+models%2E+In+International+Conference+on+Learning+Representations%2E)
21. Opher Lieber, Or Sharir, Barak Lenz, and Yoav Shoham. 2021. Jurassic-1: Technical details and evaluation. [find](https://scholar.google.com/scholar?q=Opher+Lieber%2C+Or+Sharir%2C+Barak+Lenz%2C+and+Yoav+Shoham%2E+2021%2E+Jurassic%2D1%3A+Technical+details+and+evaluation%2E)
22. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019. [Natural…](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/)
23. Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, and Rodrigo Nogueira. 2021. Pyserini: A Python toolkit for reproducible information retrieval research with sparse and dense representations. In Proceedings of the 44th International ACM SIGIR Conference on Research and Develop… [find](https://scholar.google.com/scholar?q=Jimmy+Lin%2C+Xueguang+Ma%2C+Sheng%2DChieh+Lin%2C+Jheng%2DHong+Yang%2C+Ronak+Pradeep%2C+and+Rodrigo+Nogueira%2E+2021%2E+Pyserini%3A+A+Python+toolkit+for+reproducible+information+retrieval+research+with+sparse+and+dense+re)
24. Yoav Levine, Itay Dalmedigos, Ori Ram, Yoel Zeldes, Daniel Jannai, Dor Muhlgay, Yoni Osin, Opher Lieber, Barak Lenz, Shai Shalev-Shwartz, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2022a. Standing on the shoulders of giant frozen language models. 10.48550/ARXIV.2204.10019. https://doi.org/1… — [doi:10.48550/arxiv.2204.10019](https://doi.org/10.48550/arxiv.2204.10019) [find](https://scholar.google.com/scholar?q=Yoav+Levine%2C+Itay+Dalmedigos%2C+Ori+Ram%2C+Yoel+Zeldes%2C+Daniel+Jannai%2C+Dor+Muhlgay%2C+Yoni+Osin%2C+Opher+Lieber%2C+Barak+Lenz%2C+Shai+Shalev%2DShwartz%2C+Amnon+Shashua%2C+Kevin+Leyton%2DBrown%2C+and+Yoav+Shoham%2E+2022a%2E+Sta)
25. Stephanie Lin, Jacob Hilton, and Owain Evans. 2022. [TruthfulQA: Measuring how models mimic human falsehoods](https://md.research.elamur.ai/free/en/papers/lin-2022-truthfulqa-measuring-how-models-mimic/). In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3214–3252, Dublin, Ireland. Association for Computational Linguistics…
26. Yoav Levine, Ori Ram, Daniel Jannai, Barak Lenz, Shai Shalev-Shwartz, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2022b. Huge frozen language models as readers for open-domain question answering. In ICML 2022 Workshop on Knowledge Retrieval and Language Models. [find](https://scholar.google.com/scholar?q=Yoav+Levine%2C+Ori+Ram%2C+Daniel+Jannai%2C+Barak+Lenz%2C+Shai+Shalev%2DShwartz%2C+Amnon+Shashua%2C+Kevin+Leyton%2DBrown%2C+and+Yoav+Shoham%2E+2022b%2E+Huge+frozen+language+models+as+readers+for+open%2Ddomain+question+answeri)
27. Yoav Levine, Noam Wies, Daniel Jannai, Dan Navon, Yedid Hoshen, and Amnon Shashua. 2022c. The inductive bias of in-context learning: Rethinking pretraining example design. In International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Yoav+Levine%2C+Noam+Wies%2C+Daniel+Jannai%2C+Dan+Navon%2C+Yedid+Hoshen%2C+and+Amnon+Shashua%2E+2022c%2E+The+inductive+bias+of+in%2Dcontext+learning%3A+Rethinking+pretraining+example+design%2E+In+International+Conference+)
28. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 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 Pr…
29. Zonglin Li, Ruiqi Guo, and Sanjiv Kumar. 2022. Decoupled context processing for context augmented language modeling. In Advances in Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Zonglin+Li%2C+Ruiqi+Guo%2C+and+Sanjiv+Kumar%2E+2022%2E+Decoupled+context+processing+for+context+augmented+language+modeling%2E+In+Advances+in+Neural+Information+Processing+Systems%2E)
30. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. [RoBERTa: A robustly optimized bert pretraining approach](https://md.research.elamur.ai/free/en/papers/liu-2019-roberta-robustly-optimized-bert-pretraining/). https://doi.org/10.48550/arXiv.1907.11692 [↗](https://doi.org/10.48550/arXiv.1907.11692)
31. Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020. [On faithfulness and factuality in abstractive summarization](https://md.research.elamur.ai/free/en/papers/maynez-2020-faithfulness-factuality-abstractive-summarization/). In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1906–1919, Online. Association for Computational Linguistics. https://d…
32. Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016. [Pointer sentinel mixture models](https://md.research.elamur.ai/free/en/papers/merity-2016-pointer-sentinel-mixture-models/). https://doi.org/10.48550/arXiv.1609.07843 [↗](https://doi.org/10.48550/arXiv.1609.07843)
33. Dor Muhlgay, Ori Ram, Inbal Magar, Yoav Levine, Nir Ratner, Yonatan Belinkov, Omri Abend, Kevin Leyton-Brown, Amnon Shashua, and Yoav Shoham. 2023. [Generating benchmarks for factuality evaluation of language models](https://md.research.elamur.ai/free/en/papers/muhlgay-2024-generating-benchmarks-factuality-evaluation-language/). https://doi.org/10.48550/arXiv .2307.06908 [↗](https://doi.org/10.48550/arXiv)
34. Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, Vassilis Plachouras, Tim Rocktäschel, and Sebastian Riedel. 2021. [KILT: A benchmark for knowledge intensive language tasks](https://md.research.elamur.ai/free/en/papers/petroni-2020-kilt-benchmark-knowledge-intensive-language/). In Proceedings of t…
35. Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. [find](https://scholar.google.com/scholar?q=Alec+Radford%2C+Karthik+Narasimhan%2C+Tim+Salimans%2C+and+Ilya+Sutskever%2E+2018%2E+Improving+language+understanding+by+generative+pre%2Dtraining%2E)
36. Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. [find](https://scholar.google.com/scholar?q=Alec+Radford%2C+Jeff+Wu%2C+Rewon+Child%2C+David+Luan%2C+Dario+Amodei%2C+and+Ilya+Sutskever%2E+2019%2E+Language+models+are+unsupervised+multitask+learners%2E)
37. Ori Ram, Gal Shachaf, Omer Levy, Jonathan Berant, and Amir Globerson. 2022. Learning to retrieve passages without supervision. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2687–2700, Seattle,… [find](https://scholar.google.com/scholar?q=Ori+Ram%2C+Gal+Shachaf%2C+Omer+Levy%2C+Jonathan+Berant%2C+and+Amir+Globerson%2E+2022%2E+Learning+to+retrieve+passages+without+supervision%2E+In+Proceedings+of+the+2022+Conference+of+the+North+American+Chapter+of+th)
38. Nir Ratner, Yoav Levine, Yonatan Belinkov, Ori Ram, Omri Abend, Ehud Karpas, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2022. [Parallel context windows improve in-context learning of large language models](https://md.research.elamur.ai/free/en/papers/ratner-2023-parallel-context-windows-large-language/). https://doi.org/10 .18653/v1/2023.acl-long.352
39. Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: BM25 and beyond. Foundations and Trends in Information Retrieval, 3(4):333–389. https://doi.org/10.1561/1500000019 [↗](https://doi.org/10.1561/1500000019)
40. Devendra Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, and Luke Zettlemoyer. 2022. [Improving passage retrieval with zero-shot question generation](https://md.research.elamur.ai/free/en/papers/sachan-2022-improving-passage-retrieval-zero-shot/). In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3781–3797, Abu Dhabi,…
41. Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen tau Yih. 2023. [REPLUG: Retrieval-augmented black-box language models](https://md.research.elamur.ai/free/en/papers/shi-2023-replug-retrieval-augmented-black-box/). https://doi.org/10.48550/arXiv.2301.12652 [↗](https://doi.org/10.48550/arXiv.2301.12652)
42. Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych. 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, volume 1. [find](https://scholar.google.com/scholar?q=Nandan+Thakur%2C+Nils+Reimers%2C+Andreas+Ru%CC%88ckle%CC%81%2C+Abhishek+Srivastava%2C+and+Iryna+Gurevych%2E+2021%2E+BEIR%3A+A+heterogeneous+benchmark+for+zero%2Dshot+evaluation+of+information+retrieval+models%2E+In+Proceedings+o)
43. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. [LLaMA: Open and efficient foundation language models](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-open-efficient-foundation-language/). https:…
44. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Łukasz Kaiser, and Illia Polosukhin. 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, pages 5998–6008.
45. Ben Wang and Aran Komatsuzaki. 2021. GPT-J-6B: A 6 billion parameter autoregressive language model. [find](https://scholar.google.com/scholar?q=Ben+Wang+and+Aran+Komatsuzaki%2E+2021%2E+GPT%2DJ%2D6B%3A+A+6+billion+parameter+autoregressive+language+model%2E)
46. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoe… — [Transformers: State-of-the-Art Natural Language Processing](https://md.research.elamur.ai/free/en/papers/wolf-2020-transformers-state-art-natural-language/)
47. Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019. [Defending against neural fake news](https://md.research.elamur.ai/free/en/papers/zellers-2019-defending-against-neural-fake-news/). In Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc.
48. Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. 2022. [OPT: Open pre-trai…](https://md.research.elamur.ai/free/en/papers/zhang-2022-opt-open-pre-trained-transformer/)
49. Zexuan Zhong, Tao Lei, and Danqi Chen. 2022. Training language models with memory augmentation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5657–5673, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. https://doi.org/10.… [find](https://scholar.google.com/scholar?q=Zexuan+Zhong%2C+Tao+Lei%2C+and+Danqi+Chen%2E+2022%2E+Training+language+models+with+memory+augmentation%2E+In+Proceedings+of+the+2022+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pages+5657%E2%80%9356)

*Updated 31 October 2026*