---
title: "In-context Learning with Retrieved Demonstrations for Language"
description: "Large language models can solve new tasks from a few input–output examples placed in the prompt. 151 references, cited by 48 papers here."
authors: ["Man Luo", "Xin Xu", "Yue Liu", "Panupong Pasupat", "Mehran Kazemi"]
year: 2024
venue: "arXiv:2401.11624"
source: "https://arxiv.org/abs/2401.11624"
doi: "10.48550/arxiv.2401.11624"
arxiv: "2401.11624"
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/luo-2024-context-learning-retrieved-demonstrations-language/"
---
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# In-context Learning with Retrieved Demonstrations for Language Models: A Survey

**Authors:** [Man Luo](https://md.research.elamur.ai/free/en/authors/man-luo/), [Xin Xu](https://md.research.elamur.ai/free/en/authors/xin-xu/), [Yue Liu](https://md.research.elamur.ai/free/en/authors/yue-liu/), [Panupong Pasupat](https://md.research.elamur.ai/free/en/authors/panupong-pasupat/) and [Mehran Kazemi](https://md.research.elamur.ai/free/en/authors/mehran-kazemi/) · **Year:** 2024 · **Venue:** arXiv:2401.11624 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2401.11624) · [PDF](https://arxiv.org/pdf/2401.11624) · [DOI 10.48550/arxiv.2401.11624](https://doi.org/10.48550/arxiv.2401.11624) · [arXiv 2401.11624](https://arxiv.org/abs/2401.11624) · [OpenAlex](https://openalex.org/W4391211792)

**Citations:** 6 citations (OpenAlex, 2026-10-14) · [48](https://md.research.elamur.ai/free/en/papers/luo-2024-context-learning-retrieved-demonstrations-language/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Large language models can solve new tasks from a few input–output examples placed in the prompt. This is called few-shot in-context learning (ICL), and its accuracy depends heavily on which examples are chosen. Early approaches used one fixed demonstration set per task, written by hand, sampled at random or picked by heuristics. This survey by Luo et al. reviews retrieval-based ICL (RetICL), where a retriever picks demonstrations separately for every incoming query. The paper adds no new data or model. Instead, it organizes 22 representative studies along several dimensions:
- the retrieval objective (similarity or diversity);
- how several demonstrations are assembled (single pass, clustering or iterative);
- the origin of the retrieval pool;
- off-the-shelf versus fine-tuned retrievers and their training losses;
- target applications.

The authors find that query-specific retrieval usually outperforms random or static demonstrations, but no retriever type wins on every task. For newcomers, the survey works as a map of a fragmented field with inconsistent terminology. It also lists open problems that merit further work.

## Key points
- The survey compares 22 RetICL papers in a single table (Table 1) by LLM, retrieval method, corpus, evaluation tasks and training strategy. Examples include KATE (RoBERTa+kNN), EPR (fine-tuned retriever, InfoNCE loss), CEIL (diversity training), Dr.ICL (PaLM/Flan-PaLM with BM25 and GTR), UDR, LLM-R and RetICL (LSTM trained with PPO).
- Off-the-shelf retrievers covered include BM25, SBERT, SimCSE, ColBERTv2 and multilingual SBERT. Fine-tuned retrievers are trained with list-wise ranking, InfoNCE, KL-divergence distillation, iterative training, diversity training or re-rankers.
- Retrieval pools are grouped into five types:
  - in-domain training data;
  - cross-domain data;
  - mixed-domain data;
  - free-form corpora;
  - unlabeled queries with LLM-generated chain-of-thought.
- Training signals for retrievers usually come from LLM scoring of positive and negative demonstrations. This is costly, and it is often unclear what counts as a positive example.
- Proposed future directions:
  - active updating of the retrieval corpus;
  - RetICL for small LMs;
  - theory of why similar demonstrations help;
  - fine-tuning LLMs for RetICL;
  - vision-and-language models.

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

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- [Google](https://md.research.elamur.ai/free/en/companies/google/) — Company, Mountain View, United States · 430 works · 424,363 citations
- [Arizona State University](https://md.research.elamur.ai/free/en/universities/arizona-state-university/) — University, Tempe, United States · 50 works · 2,032 citations

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## Cite

Cite as: Man Luo, Xin Xu, Yue Liu, Panupong Pasupat and Mehran Kazemi. 2024. In-context Learning with Retrieved Demonstrations for Language Models: A Survey. arXiv:2401.11624.

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

```bibtex
@misc{luo2024context,
  title = {{In-context Learning with Retrieved Demonstrations for Language Models: A Survey}},
  author = {Man Luo, Xin Xu, Yue Liu, Panupong Pasupat and Mehran Kazemi},
  year = {2024},
  doi = {10.48550/arxiv.2401.11624},
  eprint = {2401.11624},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2401.11624}
}
```

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## References

151 references, 96 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Abdalghani Abujabal, Rishiraj Saha Roy, Mohamed Yahya, and Gerhard Weikum. 2019. ComQA: A community-sourced dataset for complex factoid question answering with paraphrase clusters. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: H… [find](https://scholar.google.com/scholar?q=Abdalghani+Abujabal%2C+Rishiraj+Saha+Roy%2C+Mohamed+Yahya%2C+and+Gerhard+Weikum%2E+2019%2E+ComQA%3A+A+community%2Dsourced+dataset+for+complex+factoid+question+answering+with+paraphrase+clusters%2E+In+Proceedings+of+t)
2. Sweta Agrawal, Chunting Zhou, Mike Lewis, Luke Zettlemoyer, and Marjan Ghazvininejad. 2022. [In-context examples selection for machine translation](https://md.research.elamur.ai/free/en/papers/agrawal-2023-context-examples-selection-machine-translation/). arXiv preprint arXiv:2212.02437.
3. Sweta Agrawal, Chunting Zhou, Mike Lewis, Luke Zettlemoyer, and Marjan Ghazvininejad. 2023. [In-context examples selection for machine translation](https://md.research.elamur.ai/free/en/papers/agrawal-2023-context-examples-selection-machine-translation/). In Findings of the Association for Computational Linguistics: ACL 2023, pages 8857–8873.
4. Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. 2022. [Flamingo: a visual language model for few-shot learning](https://md.research.elamur.ai/free/en/papers/alayrac-2022-flamingo-visual-language-model-few/). Advances in neural information processing systems, 35:23716–23736.
5. Ben Taskar Alex Kulesz. 2012. Determinantal point processes for machine learning. Foundations and Trends® in Machine Learning, 5:123–286. [find](https://scholar.google.com/scholar?q=Ben+Taskar+Alex+Kulesz%2E+2012%2E+Determinantal+point+processes+for+machine+learning%2E+Foundations+and+Trends%C2%AE+in+Machine+Learning%2C+5%3A123%E2%80%93286%2E)
6. Jacob Andreas, John Bufe, David Burkett, Charles Chen Jr, Josh Clausman, Jean Crawford, Kate Crim, Jordan DeLoach, Leah Dorner, Jason Eisner, et al. 2020. [Task-oriented dialogue as dataflow synthesis](https://md.research.elamur.ai/free/en/papers/andreas-2020-task-oriented-dialogue-as-dataflow/). Transactions of the Association for Computational Linguistics, 8:556–571.
7. Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre, Shiori Sagawa, et al. 2023. [Openflamingo: An open-source framework for training large autoregressive vision-language models](https://md.research.elamur.ai/free/en/papers/awadalla-2023-openflamingo-open-source-framework-training/). arXiv preprint arXiv:2308.01390.
8. Yoshua Bengio, Réjean Ducharme, and Pascal Vincent. 2000. A neural probabilistic language model. Advances in neural information processing systems, 13. [find](https://scholar.google.com/scholar?q=Yoshua+Bengio%2C+R%C3%A9jean+Ducharme%2C+and+Pascal+Vincent%2E+2000%2E+A+neural+probabilistic+language+model%2E+Advances+in+neural+information+processing+systems%2C+13%2E)
9. Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013. [Semantic parsing on freebase from question-answer pairs](https://md.research.elamur.ai/free/en/papers/berant-2013-semantic-parsing-freebase-question-answer/). In Proceedings of the 2013 conference on empirical methods in natural language processing, pages 1533–1544.
10. Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. 2020. [Piqa: Reasoning about physical commonsense in natural language](https://md.research.elamur.ai/free/en/papers/bisk-2019-piqa-reasoning-about-physical-commonsense/). In Thirty-Fourth AAAI Conference on Artificial Intelligence.
11. Samuel Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015a. [A large annotated corpus for learning natural language inference](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/). In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 632–642.
12. Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015b. [A large annotated corpus for learning natural language inference](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/). In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), page 632–642.
13. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. [Language models are few-shot learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/). Advances in neural information processing systems, 33:1877–1901.
14. Christopher J.C. Burges. 2010. From ranknet to lambdarank to lambdamart: An overview. Technical report, Microsoft Research Technical Report MSR-TR-2010-82. [find](https://scholar.google.com/scholar?q=Christopher+J%2EC%2E+Burges%2E+2010%2E+From+ranknet+to+lambdarank+to+lambdamart%3A+An+overview%2E+Technical+report%2C+Microsoft+Research+Technical+Report+MSR%2DTR%2D2010%2D82%2E)
15. Daniel M. Cer, Mona T. Diab, Eneko Agirre, Iñigo Lopez-Gazpio, and Lucia Specia. 2017. [Semeval-2017 task 1: Semantic textual similarity - multilingual and cross-lingual focused evaluation](https://md.research.elamur.ai/free/en/papers/cer-2017-semeval-2017-task-1-semantic/). CoRR, abs/1708.00055.
16. Mingda Chen, Jingfei Du, Ramakanth Pasunuru, Todor Mihaylov, Srini Iyer, Veselin Stoyanov, and Zornitsa Kozareva. 2022. Improving in-context few-shot learning via self-supervised training. arXiv preprint arXiv:2205.01703. [find](https://scholar.google.com/scholar?q=Mingda+Chen%2C+Jingfei+Du%2C+Ramakanth+Pasunuru%2C+Todor+Mihaylov%2C+Srini+Iyer%2C+Veselin+Stoyanov%2C+and+Zornitsa+Kozareva%2E+2022%2E+Improving+in%2Dcontext+few%2Dshot+learning+via+self%2Dsupervised+training%2E+arXiv+prepr)
17. Daixuan Cheng, Shaohan Huang, Junyu Bi, Yuefeng Zhan, Jianfeng Liu, Yujing Wang, Hao Sun, Furu Wei, Denvy Deng, and Qi Zhang. 2023. Uprise: Universal prompt retrieval for improving zero-shot evaluation. arXiv preprint arXiv:2303.08518. [find](https://scholar.google.com/scholar?q=Daixuan+Cheng%2C+Shaohan+Huang%2C+Junyu+Bi%2C+Yuefeng+Zhan%2C+Jianfeng+Liu%2C+Yujing+Wang%2C+Hao+Sun%2C+Furu+Wei%2C+Denvy+Deng%2C+and+Qi+Zhang%2E+2023%2E+Uprise%3A+Universal+prompt+retrieval+for+improving+zero%2Dshot+evaluatio)
18. Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2023. [Palm: Scaling language modeling with pathways](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/). Journal of Machine Learning Research, 24(240):1–113.
19. Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416. [find](https://scholar.google.com/scholar?q=Hyung+Won+Chung%2C+Le+Hou%2C+Shayne+Longpre%2C+Barret+Zoph%2C+Yi+Tay%2C+William+Fedus%2C+Eric+Li%2C+Xuezhi+Wang%2C+Mostafa+Dehghani%2C+Siddhartha+Brahma%2C+et+al%2E+2022%2E+Scaling+instruction%2Dfinetuned+language+models%2E+arXi)
20. Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019. [Boolq: Exploring the surprising difficulty of natural yes/no questions](https://md.research.elamur.ai/free/en/papers/clark-2019-boolq-exploring-surprising-difficulty-natural/). arXiv preprint arXiv:1905.10044.
21. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018. [Think you have solved question answering? try arc, the ai2 reasoning challenge](https://md.research.elamur.ai/free/en/papers/clark-2018-think-have-solved-question-answering/). ArXiv, abs/1803.05457.
22. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021. [Training verifiers to solve math word problems](https://md.research.elamur.ai/free/en/papers/cobbe-2021-training-verifiers-solve-math-word/). arXiv preprint arXiv:2110.14168.
23. Bhavana Dalvi, Oyvind Tafjord, and Peter Clark. 2022. Towards teachable reasoning systems: Using a dynamic memory of user feedback for continual system improvement. arXiv preprint arXiv:2204.13074. [find](https://scholar.google.com/scholar?q=Bhavana+Dalvi%2C+Oyvind+Tafjord%2C+and+Peter+Clark%2E+2022%2E+Towards+teachable+reasoning+systems%3A+Using+a+dynamic+memory+of+user+feedback+for+continual+system+improvement%2E+arXiv+preprint+arXiv%3A2204%2E13074%2E)
24. Marie-Catherine De Marneffe, Anna N Rafferty, and Christopher D Manning. 2008. [Finding contradictions in text](https://md.research.elamur.ai/free/en/papers/marneffe-2008-finding-contradictions-text/). In Proceedings of acl-08: Hlt, pages 1039–1047.
25. Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023. [Qlora: Efficient finetuning of quantized llms](https://md.research.elamur.ai/free/en/papers/dettmers-2023-qlora-efficient-finetuning-quantized-llms/). arXiv preprint arXiv:2305.14314.
26. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. [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/). arXiv preprint arXiv:1810.04805.
27. Bill Dolan, Chris Quirk, and Chris Brockett. 2004. [Unsupervised construction of large paraphrase corpora: exploiting massively parallel news sources](https://md.research.elamur.ai/free/en/papers/dolan-2004-unsupervised-construction-large-paraphrase-corpora/). In Proceedings of the 20th international conference on Computational Linguistics, pages 350–es.
28. Andrew Drozdov, Nathanael Schärli, Ekin Akyürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, and Denny Zhou. 2022. Compositional semantic parsing with large language models. In The Eleventh International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Andrew+Drozdov%2C+Nathanael+Sch%C3%A4rli%2C+Ekin+Aky%C3%BCrek%2C+Nathan+Scales%2C+Xinying+Song%2C+Xinyun+Chen%2C+Olivier+Bousquet%2C+and+Denny+Zhou%2E+2022%2E+Compositional+semantic+parsing+with+large+language+models%2E+In+The+Ele)
29. Andrew Drozdov, Honglei Zhuang, Zhuyun Dai, Zhen Qin, Razieh Rahimi, Xuanhui Wang, Dana Alon, Mohit Iyyer, Andrew McCallum, Donald Metzler, et al. 2023. Parade: Passage ranking using demonstrations with large language models. arXiv preprint arXiv:2310.14408. — [arXiv:2310.14408](https://arxiv.org/abs/2310.14408) [find](https://scholar.google.com/scholar?q=Andrew+Drozdov%2C+Honglei+Zhuang%2C+Zhuyun+Dai%2C+Zhen+Qin%2C+Razieh+Rahimi%2C+Xuanhui+Wang%2C+Dana+Alon%2C+Mohit+Iyyer%2C+Andrew+McCallum%2C+Donald+Metzler%2C+et+al%2E+2023%2E+Parade%3A+Passage+ranking+using+demonstrations+wi)
30. Ondřej Dušek, David M Howcroft, and Verena Rieser. 2019. [Semantic noise matters for neural natural language generation](https://md.research.elamur.ai/free/en/papers/dusek-2019-semantic-noise-matters-neural-natural/). In Proceedings of the 12th International Conference on Natural Language Generation, pages 421–426.
31. Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, and Tushar Khot. 2022. [Complexity-based prompting for multi-step reasoning](https://md.research.elamur.ai/free/en/papers/fu-2022-complexity-based-prompting-multi-step/). arXiv preprint arXiv:2210.00720.
32. Feng Gao, Qing Ping, Govind Thattai, Aishwarya Reganti, Ying Nian Wu, and Prem Natarajan. 2022. Transform-retrieve-generate: Natural language-centric outside-knowledge visual question answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5067–5… [find](https://scholar.google.com/scholar?q=Feng+Gao%2C+Qing+Ping%2C+Govind+Thattai%2C+Aishwarya+Reganti%2C+Ying+Nian+Wu%2C+and+Prem+Natarajan%2E+2022%2E+Transform%2Dretrieve%2Dgenerate%3A+Natural+language%2Dcentric+outside%2Dknowledge+visual+question+answering%2E+In+Pr)
33. Lingyu Gao, Aditi Chaudhary, Krishna Srinivasan, Kazuma Hashimoto, Karthik Raman, and Michael Bendersky. 2023. Ambiguity-aware in-context learning with large language models. arXiv preprint arXiv:2309.07900. — [arXiv:2309.07900](https://arxiv.org/abs/2309.07900) [find](https://scholar.google.com/scholar?q=Lingyu+Gao%2C+Aditi+Chaudhary%2C+Krishna+Srinivasan%2C+Kazuma+Hashimoto%2C+Karthik+Raman%2C+and+Michael+Bendersky%2E+2023%2E+Ambiguity%2Daware+in%2Dcontext+learning+with+large+language+models%2E+arXiv+preprint+arXiv%3A2309)
34. Tianyu Gao, Adam Fisch, and Danqi Chen. 2021a. [Making pre-trained language models better few-shot learners](https://md.research.elamur.ai/free/en/papers/gao-2021-making-pre-trained-language-models/). In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), p…
35. Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021b. [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, pages 6894–6910.
36. Shivam Garg, Dimitris Tsipras, Percy S Liang, and Gregory Valiant. 2022. What can transformers learn in-context? a case study of simple function classes. Advances in Neural Information Processing Systems, 35:30583–30598. [find](https://scholar.google.com/scholar?q=Shivam+Garg%2C+Dimitris+Tsipras%2C+Percy+S+Liang%2C+and+Gregory+Valiant%2E+2022%2E+What+can+transformers+learn+in%2Dcontext%3F+a+case+study+of+simple+function+classes%2E+Advances+in+Neural+Information+Processing+Syst)
37. Alec Go, Richa Bhayani, and Lei Huang. 2009. Twitter sentiment classification using distant supervision. CS224N project report, Stanford, 1(12):2009. [find](https://scholar.google.com/scholar?q=Alec+Go%2C+Richa+Bhayani%2C+and+Lei+Huang%2E+2009%2E+Twitter+sentiment+classification+using+distant+supervision%2E+CS224N+project+report%2C+Stanford%2C+1%2812%29%3A2009%2E)
38. Hila Gonen, Srini Iyer, Terra Blevins, Noah A Smith, and Luke Zettlemoyer. 2023. Demystifying prompts in language models via perplexity estimation. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10136–10148. [find](https://scholar.google.com/scholar?q=Hila+Gonen%2C+Srini+Iyer%2C+Terra+Blevins%2C+Noah+A+Smith%2C+and+Luke+Zettlemoyer%2E+2023%2E+Demystifying+prompts+in+language+models+via+perplexity+estimation%2E+In+Findings+of+the+Association+for+Computational+Lin)
39. Suchin Gururangan, Mike Lewis, Ari Holtzman, Noah A Smith, and Luke Zettlemoyer. 2022. Demix layers: Disentangling domains for modular language modeling. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,… [find](https://scholar.google.com/scholar?q=Suchin+Gururangan%2C+Mike+Lewis%2C+Ari+Holtzman%2C+Noah+A+Smith%2C+and+Luke+Zettlemoyer%2E+2022%2E+Demix+layers%3A+Disentangling+domains+for+modular+language+modeling%2E+In+Proceedings+of+the+2022+Conference+of+the+N)
40. Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020. [Retrieval augmented language model pre-training](https://md.research.elamur.ai/free/en/papers/guu-2020-realm-retrieval-augmented-language-model/). In International conference on machine learning, pages 3929–3938. PMLR.
41. Kazuma Hashimoto, Karthik Raman, and Michael Bendersky. 2023. Take one step at a time to know incremental utility of demonstration: An analysis on reranking for few-shot in-context learning. arXiv preprint arXiv:2311.09619. [find](https://scholar.google.com/scholar?q=Kazuma+Hashimoto%2C+Karthik+Raman%2C+and+Michael+Bendersky%2E+2023%2E+Take+one+step+at+a+time+to+know+incremental+utility+of+demonstration%3A+An+analysis+on+reranking+for+few%2Dshot+in%2Dcontext+learning%2E+arXiv+pre)
42. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021. [Measuring mathematical problem solving with the math dataset](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-mathematical-problem-solving-math/). In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2).
43. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735–1780. [find](https://scholar.google.com/scholar?q=Sepp+Hochreiter+and+J%C3%BCrgen+Schmidhuber%2E+1997%2E+Long+short%2Dterm+memory%2E+Neural+computation%2C+9%288%29%3A1735%E2%80%931780%2E)
44. SU Hongjin, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A Smith, et al. 2022. Selective annotation makes language models better few-shot learners. In The Eleventh International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=SU+Hongjin%2C+Jungo+Kasai%2C+Chen+Henry+Wu%2C+Weijia+Shi%2C+Tianlu+Wang%2C+Jiayi+Xin%2C+Rui+Zhang%2C+Mari+Ostendorf%2C+Luke+Zettlemoyer%2C+Noah+A+Smith%2C+et+al%2E+2022%2E+Selective+annotation+makes+language+models+better+fe)
45. Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 2021. [Lora: Low-rank adaptation of large language models](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/). In International Conference on Learning Representations.
46. Yushi Hu, Chia-Hsuan Lee, Tianbao Xie, Tao Yu, Noah A Smith, and Mari Ostendorf. 2022. [In-context learning for few-shot dialogue state tracking](https://md.research.elamur.ai/free/en/papers/hu-2022-context-learning-few-shot-dialogue/). In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 2627–2643.
47. Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 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):1–43.
48. Liwei Jiang, Jena D Hwang, Chandra Bhagavatula, Ronan Le Bras, Jenny Liang, Jesse Dodge, Keisuke Sakaguchi, Maxwell Forbes, Jon Borchardt, Saadia Gabriel, et al. 2021. Can machines learn morality? the delphi experiment. arXiv preprint arXiv:2110.07574. [find](https://scholar.google.com/scholar?q=Liwei+Jiang%2C+Jena+D+Hwang%2C+Chandra+Bhagavatula%2C+Ronan+Le+Bras%2C+Jenny+Liang%2C+Jesse+Dodge%2C+Keisuke+Sakaguchi%2C+Maxwell+Forbes%2C+Jon+Borchardt%2C+Saadia+Gabriel%2C+et+al%2E+2021%2E+Can+machines+learn+morality%3F+the)
49. Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. 2020. [How can we know what language models know?](https://md.research.elamur.ai/free/en/papers/jiang-2020-how-can-we-know-what/) Transactions of the Association for Computational Linguistics, 8:423–438.
50. Mandar Joshi, Eunsol Choi, Daniel S 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.
51. James H. Jurafsky, Dan; Martin. 2021. N-gram language models. In Speech and Language Processing (3rd ed.). [find](https://scholar.google.com/scholar?q=James+H%2E+Jurafsky%2C+Dan%3B+Martin%2E+2021%2E+N%2Dgram+language+models%2E+In+Speech+and+Language+Processing+%283rd+ed%2E%29%2E)
52. 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.
53. Mehran Kazemi, Sid Mittal, and Deepak Ramachandran. 2023a. [Understanding finetuning for factual knowledge extraction from language models](https://md.research.elamur.ai/free/en/papers/kazemi-2023-understanding-finetuning-factual-knowledge-extraction/). arXiv preprint arXiv:2301.11293.
54. Seyed Mehran Kazemi, Najoung Kim, Deepti Bhatia, Xin Xu, and Deepak Ramachandran. 2023b. [Lambada: Backward chaining for automated reasoning in natural language](https://md.research.elamur.ai/free/en/papers/kazemi-2023-lambada-backward-chaining-automated-reasoning/). In ACL.
55. Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. 2018. [Looking beyond the surface:a challenge set for reading comprehension over multiple sentences](https://md.research.elamur.ai/free/en/papers/khashabi-2018-looking-beyond-surface-challenge-set/). In Proceedings of North American Chapter of the Association for Computational Linguistics (NAACL).
56. Omar Khattab, Keshav Santhanam, Xiang Lisa Li, David Hall, Percy Liang, Christopher Potts, and Matei Zaharia. 2022. [Demonstrate-search-predict: Composing retrieval and language models for knowledge-intensive nlp](https://md.research.elamur.ai/free/en/papers/khattab-2022-demonstrate-search-predict-composing-retrieval/). arXiv preprint arXiv:2212.14024.
57. Omar Khattab and Matei Zaharia. 2020. [Colbert: Efficient and effective passage search via contextualized late interaction over BERT](https://md.research.elamur.ai/free/en/papers/khattab-2020-colbert-efficient-effective-passage/). CoRR, abs/2004.12832.
58. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. [Large language models are zero-shot reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/), 2022. URL https://arxiv.org/abs/2205.11916. [↗](https://arxiv.org/abs/2205.11916)
59. Alex Kulesza, Ben Taskar, et al. 2012. Determinantal point processes for machine learning. Foundations and Trends® in Machine Learning, 5(2–3):123–286. [find](https://scholar.google.com/scholar?q=Alex+Kulesza%2C+Ben+Taskar%2C+et+al%2E+2012%2E+Determinantal+point+processes+for+machine+learning%2E+Foundations+and+Trends%C2%AE+in+Machine+Learning%2C+5%282%E2%80%933%29%3A123%E2%80%93286%2E)
60. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, 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 Linguistic…
61. Tamera Lanham, Anna Chen, Ansh Radhakrishnan, Benoit Steiner, Carson Denison, Danny Hernandez, Dustin Li, Esin Durmus, Evan Hubinger, Jackson Kernion, et al. 2023. [Measuring faithfulness in chain-of-thought reasoning](https://md.research.elamur.ai/free/en/papers/lanham-2023-measuring-faithfulness-chain-thought-reasoning/). arXiv preprint arXiv:2307.13702.
62. Brian Lester, Rami Al-Rfou, and Noah Constant. 2021. [The power of scale for parameter-efficient prompt tuning](https://md.research.elamur.ai/free/en/papers/lester-2021-power-scale-parameter-efficient-prompt/). In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3045–3059.
63. Itay Levy, Ben Bogin, and Jonathan Berant. 2022. [Diverse demonstrations improve in-context compositional generalization](https://md.research.elamur.ai/free/en/papers/levy-2023-diverse-demonstrations-improve-context-compositional/). arXiv preprint arXiv:2212.06800.
64. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, 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/). Advances in Neural Information Processing Systems, 33:9459–9474.
65. Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, et al. 2022. [Solving quantitative reasoning problems with language models](https://md.research.elamur.ai/free/en/papers/lewkowycz-2022-solving-quantitative-reasoning-problems-language/). Advances in Neural Information Processing Systems, 35:3843–3857.
66. Bo Li, Yuanhan Zhang, Liangyu Chen, Jinghao Wang, Fanyi Pu, Jingkang Yang, Chunyuan Li, and Ziwei Liu. 2023a. Mimic-it: Multi-modal in-context instruction tuning. arXiv preprint arXiv:2306.05425. — [arXiv:2306.05425](https://arxiv.org/abs/2306.05425) [find](https://scholar.google.com/scholar?q=Bo+Li%2C+Yuanhan+Zhang%2C+Liangyu+Chen%2C+Jinghao+Wang%2C+Fanyi+Pu%2C+Jingkang+Yang%2C+Chunyuan+Li%2C+and+Ziwei+Liu%2E+2023a%2E+Mimic%2Dit%3A+Multi%2Dmodal+in%2Dcontext+instruction+tuning%2E+arXiv+preprint+arXiv%3A2306%2E05425%2E+%E2%80%94+%5Ba)
67. Haoran Li, Abhinav Arora, Shuohui Chen, Anchit Gupta, Sonal Gupta, and Yashar Mehdad. 2021. [Mtop: A comprehensive multilingual task-oriented semantic parsing benchmark](https://md.research.elamur.ai/free/en/papers/li-2021-mtop-comprehensive-multilingual-task-oriented/). In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages…
68. Junlong Li, Zhuosheng Zhang, and Hai Zhao. 2022. Self-prompting large language models for zero-shot open-domain qa. arXiv preprint arXiv:2212.08635. [find](https://scholar.google.com/scholar?q=Junlong+Li%2C+Zhuosheng+Zhang%2C+and+Hai+Zhao%2E+2022%2E+Self%2Dprompting+large+language+models+for+zero%2Dshot+open%2Ddomain+qa%2E+arXiv+preprint+arXiv%3A2212%2E08635%2E)
69. Xiaonan Li, Kai Lv, Hang Yan, Tianyang Lin, Wei Zhu, Yuan Ni, Guotong Xie, Xiaoling Wang, and Xipeng Qiu. 2023b. [Unified demonstration retriever for in-context learning](https://md.research.elamur.ai/free/en/papers/li-2023-unified-demonstration-retriever-context-learning/). arXiv preprint arXiv:2305.04320.
70. Xiaonan Li and Xipeng Qiu. 2023a. Finding supporting examples for in-context learning. arXiv preprint arXiv:2302.13539. — [arXiv:2302.13539](https://arxiv.org/abs/2302.13539) [find](https://scholar.google.com/scholar?q=Xiaonan+Li+and+Xipeng+Qiu%2E+2023a%2E+Finding+supporting+examples+for+in%2Dcontext+learning%2E+arXiv+preprint+arXiv%3A2302%2E13539%2E+%E2%80%94+%5BarXiv%3A2302%2E13539%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2302%2E13539%29)
71. Xiaonan Li and Xipeng Qiu. 2023b. Mot: Pre-thinking and recalling enable chatgpt to self-improve with memory-of-thoughts. arXiv preprint arXiv:2305.05181. — [arXiv:2305.05181](https://arxiv.org/abs/2305.05181) [find](https://scholar.google.com/scholar?q=Xiaonan+Li+and+Xipeng+Qiu%2E+2023b%2E+Mot%3A+Pre%2Dthinking+and+recalling+enable+chatgpt+to+self%2Dimprove+with+memory%2Dof%2Dthoughts%2E+arXiv+preprint+arXiv%3A2305%2E05181%2E+%E2%80%94+%5BarXiv%3A2305%2E05181%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F23)
72. Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren. 2020. [Birds have four legs?! numersense: Probing numerical commonsense knowledge of pre-trained language models](https://md.research.elamur.ai/free/en/papers/lin-2020-birds-have-four-legs-numersense/). arXiv preprint arXiv:2005.00683.
73. Xi Victoria Lin, Chenglong Wang, Luke Zettlemoyer, and Michael D Ernst. 2018. Nl2bash: A corpus and semantic parser for natural language interface to the linux operating system. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). [find](https://scholar.google.com/scholar?q=Xi+Victoria+Lin%2C+Chenglong+Wang%2C+Luke+Zettlemoyer%2C+and+Michael+D+Ernst%2E+2018%2E+Nl2bash%3A+A+corpus+and+semantic+parser+for+natural+language+interface+to+the+linux+operating+system%2E+In+Proceedings+of+the+)
74. Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017. Program induction by rationale generation: Learning to solve and explain algebraic word problems. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 158–167. [find](https://scholar.google.com/scholar?q=Wang+Ling%2C+Dani+Yogatama%2C+Chris+Dyer%2C+and+Phil+Blunsom%2E+2017%2E+Program+induction+by+rationale+generation%3A+Learning+to+solve+and+explain+algebraic+word+problems%2E+In+Proceedings+of+the+55th+Annual+Meetin)
75. Jiachang Liu, Dinghan Shen, Yizhe Zhang, William B Dolan, Lawrence Carin, and Weizhu Chen. 2022. [What makes good in-context examples for gpt-3?](https://md.research.elamur.ai/free/en/papers/liu-2022-what-makes-good-context-examples/) In Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 10…
76. 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/). arXiv preprint arXiv:1907.11692.
77. Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V Le, Barret Zoph, Jason Wei, et al. 2023. [The flan collection: Designing data and methods for effective instruction tuning](https://md.research.elamur.ai/free/en/papers/longpre-2023-flan-collection-designing-data-methods/). arXiv preprint arXiv:2301.13688.
78. Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, and Ashwin Kalyan. 2022a. [Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning](https://md.research.elamur.ai/free/en/papers/lu-2022-dynamic-prompt-learning-policy-gradient/). In The Eleventh International Conference on Learning Representations.
79. Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022b. [Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity](https://md.research.elamur.ai/free/en/papers/lu-2022-fantastically-ordered-prompts-where-find/). In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers),…
80. Man Luo, Xin Xu, Zhuyun Dai, Panupong Pasupat, Mehran Kazemi, Chitta Baral, Vaiva Imbrasaite, and Vincent Y Zhao. 2023. Dr. icl: Demonstration-retrieved in-context learning. arXiv preprint arXiv:2305.14128. — [arXiv:2305.14128](https://arxiv.org/abs/2305.14128) [find](https://scholar.google.com/scholar?q=Man+Luo%2C+Xin+Xu%2C+Zhuyun+Dai%2C+Panupong+Pasupat%2C+Mehran+Kazemi%2C+Chitta+Baral%2C+Vaiva+Imbrasaite%2C+and+Vincent+Y+Zhao%2E+2023%2E+Dr%2E+icl%3A+Demonstration%2Dretrieved+in%2Dcontext+learning%2E+arXiv+preprint+arXiv%3A2305%2E)
81. Man Luo, Yankai Zeng, Pratyay Banerjee, and Chitta Baral. 2021. Weakly-supervised visual-retriever-reader for knowledge-based question answering. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6417–6431. [find](https://scholar.google.com/scholar?q=Man+Luo%2C+Yankai+Zeng%2C+Pratyay+Banerjee%2C+and+Chitta+Baral%2E+2021%2E+Weakly%2Dsupervised+visual%2Dretriever%2Dreader+for+knowledge%2Dbased+question+answering%2E+In+Proceedings+of+the+2021+Conference+on+Empirical+Met)
82. Xinxi Lyu, Sewon Min, Iz Beltagy, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2022. Z-icl: Zero-shot in-context learning with pseudo-demonstrations. arXiv preprint arXiv:2212.09865. [find](https://scholar.google.com/scholar?q=Xinxi+Lyu%2C+Sewon+Min%2C+Iz+Beltagy%2C+Luke+Zettlemoyer%2C+and+Hannaneh+Hajishirzi%2E+2022%2E+Z%2Dicl%3A+Zero%2Dshot+in%2Dcontext+learning+with+pseudo%2Ddemonstrations%2E+arXiv+preprint+arXiv%3A2212%2E09865%2E)
83. Huan Ma, Changqing Zhang, Yatao Bian, Lemao Liu, Zhirui Zhang, Peilin Zhao, Shu Zhang, Huazhu Fu, Qinghua Hu, and Bingzhe Wu. 2023. [Fairness-guided few-shot prompting for large language models](https://md.research.elamur.ai/free/en/papers/ma-2023-fairness-guided-few-shot-prompting/). arXiv preprint arXiv:2303.13217.
84. Aman Madaan, Niket Tandon, Peter Clark, and Yiming Yang. 2022. Memory-assisted prompt editing to improve gpt-3 after deployment. arXiv preprint arXiv:2201.06009. [find](https://scholar.google.com/scholar?q=Aman+Madaan%2C+Niket+Tandon%2C+Peter+Clark%2C+and+Yiming+Yang%2E+2022%2E+Memory%2Dassisted+prompt+editing+to+improve+gpt%2D3+after+deployment%2E+arXiv+preprint+arXiv%3A2201%2E06009%2E)
85. Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018. Can a suit of armor conduct electricity? a new dataset for open book question answering. In EMNLP. [find](https://scholar.google.com/scholar?q=Todor+Mihaylov%2C+Peter+Clark%2C+Tushar+Khot%2C+and+Ashish+Sabharwal%2E+2018%2E+Can+a+suit+of+armor+conduct+electricity%3F+a+new+dataset+for+open+book+question+answering%2E+In+EMNLP%2E)
86. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. [Efficient estimation of word representations in vector space](https://md.research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/). arXiv preprint arXiv:1301.3781.
87. Aristides Milios, Siva Reddy, and Dzmitry Bahdanau. 2023. In-context learning for text classification with many labels. In Proceedings of the 1st GenBench Workshop on (Benchmarking) Generalisation in NLP, pages 173–184. [find](https://scholar.google.com/scholar?q=Aristides+Milios%2C+Siva+Reddy%2C+and+Dzmitry+Bahdanau%2E+2023%2E+In%2Dcontext+learning+for+text+classification+with+many+labels%2E+In+Proceedings+of+the+1st+GenBench+Workshop+on+%28Benchmarking%29+Generalisation+in+)
88. Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2022a. Metaicl: Learning to learn in context. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2791–2809. [find](https://scholar.google.com/scholar?q=Sewon+Min%2C+Mike+Lewis%2C+Luke+Zettlemoyer%2C+and+Hannaneh+Hajishirzi%2E+2022a%2E+Metaicl%3A+Learning+to+learn+in+context%2E+In+Proceedings+of+the+2022+Conference+of+the+North+American+Chapter+of+the+Association+f)
89. Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022b. [Rethinking the role of demonstrations: What makes in-context learning work?](https://md.research.elamur.ai/free/en/papers/min-2022-rethinking-role-demonstrations-what-makes/) arXiv preprint arXiv:2202.12837.
90. Ranjita Naik, Varun Chandrasekaran, Mert Yuksekgonul, Hamid Palangi, and Besmira Nushi. 2023. Diversity of thought improves reasoning abilities of large language models. arXiv preprint arXiv:2310.07088. — [arXiv:2310.07088](https://arxiv.org/abs/2310.07088) [find](https://scholar.google.com/scholar?q=Ranjita+Naik%2C+Varun+Chandrasekaran%2C+Mert+Yuksekgonul%2C+Hamid+Palangi%2C+and+Besmira+Nushi%2E+2023%2E+Diversity+of+thought+improves+reasoning+abilities+of+large+language+models%2E+arXiv+preprint+arXiv%3A2310%2E0708)
91. Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, et al. 2021. [Dart: Open-domain structured data record to text generation](https://md.research.elamur.ai/free/en/papers/nan-2020-dart-open-domain-structured-data/). In Proceedings of the 2021 Conference of the North American Chapter of the Associati…
92. Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016. [Ms marco: A human generated machine reading comprehension dataset](https://md.research.elamur.ai/free/en/papers/bajaj-2016-ms-marco-human-generated/). choice, 2640:660.
93. Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ji Ma, Vincent Y Zhao, Yi Luan, Keith B Hall, Ming-Wei Chang, et al. 2021. [Large dual encoders are generalizable retrievers](https://md.research.elamur.ai/free/en/papers/ni-2021-large-dual-encoders-generalizable-retrievers/). arXiv preprint arXiv:2112.07899.
94. Ercong Nie, Sheng Liang, Helmut Schmid, and Hinrich Schütze. 2022. Cross-lingual retrieval augmented prompt for low-resource languages. ArXiv, abs/2212.09651. [find](https://scholar.google.com/scholar?q=Ercong+Nie%2C+Sheng+Liang%2C+Helmut+Schmid%2C+and+Hinrich+Sch%C3%BCtze%2E+2022%2E+Cross%2Dlingual+retrieval+augmented+prompt+for+low%2Dresource+languages%2E+ArXiv%2C+abs%2F2212%2E09651%2E)
95. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. [Training language models to follow instructions with human feedback](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/). Advances in Neural Information Processing Systems, 35:27730–27744.
96. Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, and Dipanjan Das. 2020. [Totto: A controlled table-to-text generation dataset](https://md.research.elamur.ai/free/en/papers/parikh-2020-totto-controlled-table-text-generation/). In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1173–1186.
97. Yingzhe Peng, Xu Yang, Haoxuan Ma, Shuo Xu, Chi Zhang, Yucheng Han, and Hanwang Zhang. 2023. Icd-lm: Configuring vision-language in-context demonstrations by language modeling. arXiv preprint arXiv:2312.10104. — [arXiv:2312.10104](https://arxiv.org/abs/2312.10104) [find](https://scholar.google.com/scholar?q=Yingzhe+Peng%2C+Xu+Yang%2C+Haoxuan+Ma%2C+Shuo+Xu%2C+Chi+Zhang%2C+Yucheng+Han%2C+and+Hanwang+Zhang%2E+2023%2E+Icd%2Dlm%3A+Configuring+vision%2Dlanguage+in%2Dcontext+demonstrations+by+language+modeling%2E+arXiv+preprint+arXiv%3A23)
98. Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel. 2019. [Language models as knowledge bases?](https://md.research.elamur.ai/free/en/papers/petroni-2019-language-models-as-knowledge-bases/) arXiv preprint arXiv:1909.01066.
99. Gabriel Poesia, Alex Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, and Sumit Gulwani. 2021. Synchromesh: Reliable code generation from pre-trained language models. In International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Gabriel+Poesia%2C+Alex+Polozov%2C+Vu+Le%2C+Ashish+Tiwari%2C+Gustavo+Soares%2C+Christopher+Meek%2C+and+Sumit+Gulwani%2E+2021%2E+Synchromesh%3A+Reliable+code+generation+from+pre%2Dtrained+language+models%2E+In+International+)
100. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9. [find](https://scholar.google.com/scholar?q=Alec+Radford%2C+Jeffrey+Wu%2C+Rewon+Child%2C+David+Luan%2C+Dario+Amodei%2C+Ilya+Sutskever%2C+et+al%2E+2019%2E+Language+models+are+unsupervised+multitask+learners%2E+OpenAI+blog%2C+1%288%29%3A9%2E)
101. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. [Exploring the limits of transfer learning with a unified text-to-text transformer](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/). The Journal of Machine Learning Research, 21(1):5485–5551.
102. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. [Squad: 100,000+ questions for machine comprehension of text](https://md.research.elamur.ai/free/en/papers/rajpurkar-2016-squad-100-000-questions-machine/). arXiv preprint arXiv:1606.05250.
103. Nils Reimers and Iryna Gurevych. 2019a. [Sentence-bert: Sentence embeddings using siamese bert-networks](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/). In Empirical Methods in Natural Language Processing and International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3982––3992.
104. Nils Reimers and Iryna Gurevych. 2019b. [Sentence-bert: Sentence embeddings using siamese bert-networks](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/). arXiv preprint arXiv:1908.10084.
105. Stephen Robertson, Hugo Zaragoza, et al. 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=Stephen+Robertson%2C+Hugo+Zaragoza%2C+et+al%2E+2009%2E+The+probabilistic+relevance+framework%3A+Bm25+and+beyond%2E+Foundations+and+Trends%C2%AE+in+Information+Retrieval%2C+3%284%29%3A333%E2%80%93389%2E)
106. Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S Gordon. 2011. Choice of plausible alternatives: An evaluation of commonsense causal reasoning. In 2011 AAAI Spring Symposium Series. [find](https://scholar.google.com/scholar?q=Melissa+Roemmele%2C+Cosmin+Adrian+Bejan%2C+and+Andrew+S+Gordon%2E+2011%2E+Choice+of+plausible+alternatives%3A+An+evaluation+of+commonsense+causal+reasoning%2E+In+2011+AAAI+Spring+Symposium+Series%2E)
107. Ronald Rosenfeld. 2000. Two decades of statistical language modeling: Where do we go from here? Proceedings of the IEEE, 88(8):1270–1278. [find](https://scholar.google.com/scholar?q=Ronald+Rosenfeld%2E+2000%2E+Two+decades+of+statistical+language+modeling%3A+Where+do+we+go+from+here%3F+Proceedings+of+the+IEEE%2C+88%288%29%3A1270%E2%80%931278%2E)
108. Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2022. [Learning to retrieve prompts for in-context learning](https://md.research.elamur.ai/free/en/papers/rubin-2021-learning-retrieve-prompts-context-learning/). In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2655–2671.
109. Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, and Matei Zaharia. 2021. [Colbertv2: Effective and efficient retrieval via lightweight late interaction](https://md.research.elamur.ai/free/en/papers/santhanam-2022-colbertv2-effective-efficient-retrieval-lightweight/). CoRR, abs/2112.01488.
110. Justyna Sarzynska-Wawer, Aleksander Wawer, Aleksandra Pawlak, Julia Szymanowska, Izabela Stefaniak, Michal Jarkiewicz, and Lukasz Okruszek. 2021. Detecting formal thought disorder by deep contextualized word representations. Psychiatry Research, 304:114135. [find](https://scholar.google.com/scholar?q=Justyna+Sarzynska%2DWawer%2C+Aleksander+Wawer%2C+Aleksandra+Pawlak%2C+Julia+Szymanowska%2C+Izabela+Stefaniak%2C+Michal+Jarkiewicz%2C+and+Lukasz+Okruszek%2E+2021%2E+Detecting+formal+thought+disorder+by+deep+contextualiz)
111. Alexander Scarlatos and Andrew Lan. 2023. Reticl: Sequential retrieval of in-context examples with reinforcement learning. arXiv preprint arXiv:2305.14502. — [arXiv:2305.14502](https://arxiv.org/abs/2305.14502) [find](https://scholar.google.com/scholar?q=Alexander+Scarlatos+and+Andrew+Lan%2E+2023%2E+Reticl%3A+Sequential+retrieval+of+in%2Dcontext+examples+with+reinforcement+learning%2E+arXiv+preprint+arXiv%3A2305%2E14502%2E+%E2%80%94+%5BarXiv%3A2305%2E14502%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2)
112. Peng Shi, Rui Zhang, He Bai, and Jimmy Lin. 2022. Xricl: Cross-lingual retrieval-augmented in-context learning for cross-lingual text-to-sql semantic parsing. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5248–5259. [find](https://scholar.google.com/scholar?q=Peng+Shi%2C+Rui+Zhang%2C+He+Bai%2C+and+Jimmy+Lin%2E+2022%2E+Xricl%3A+Cross%2Dlingual+retrieval%2Daugmented+in%2Dcontext+learning+for+cross%2Dlingual+text%2Dto%2Dsql+semantic+parsing%2E+In+Findings+of+the+Association+for+Comput)
113. Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020. [Autoprompt: Eliciting knowledge from language models with automatically generated prompts](https://md.research.elamur.ai/free/en/papers/shin-2020-autoprompt-eliciting-knowledge-language-models/). arXiv preprint arXiv:2010.15980.
114. Suzanna Sia and Kevin Duh. 2023. In-context learning as maintaining coherency: A study of onthe-fly machine translation using large language models. In Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track, pages 173–185. [find](https://scholar.google.com/scholar?q=Suzanna+Sia+and+Kevin+Duh%2E+2023%2E+In%2Dcontext+learning+as+maintaining+coherency%3A+A+study+of+onthe%2Dfly+machine+translation+using+large+language+models%2E+In+Proceedings+of+Machine+Translation+Summit+XIX%2C+V)
115. Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013. [Recursive deep models for semantic compositionality over a sentiment treebank](https://md.research.elamur.ai/free/en/papers/socher-2013-recursive-deep-models-semantic-compositionality/). In Proceedings of the 2013 conference on empirical methods in natural language processing, pages 1631…
116. Mujeen Sung, Jinhyuk Lee, Sean Yi, Minji Jeon, Sungdong Kim, and Jaewoo Kang. 2021. [Can language models be biomedical knowledge bases?](https://md.research.elamur.ai/free/en/papers/sung-2021-can-language-models-be-biomedical/) arXiv preprint arXiv:2109.07154.
117. Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, et al. 2022. [Challenging big-bench tasks and whether chain-of-thought can solve them](https://md.research.elamur.ai/free/en/papers/suzgun-2023-challenging-big-bench-tasks-whether/). arXiv preprint arXiv:2210.09261.
118. Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019. [Commonsenseqa: A question answering challenge targeting commonsense knowledge](https://md.research.elamur.ai/free/en/papers/talmor-2019-commonsenseqa-question-answering-challenge-targeting/). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,…
119. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N 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/). Advances in neural information processing systems, 30.
120. Johannes Von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, and Max Vladymyrov. 2023. [Transformers learn in-context by gradient descent](https://md.research.elamur.ai/free/en/papers/oswald-2022-transformers-learn-context-gradient-descent/). In International Conference on Machine Learning, pages 35151–35174. PMLR.
121. Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan O Arik, and Tomas Pfister. 2023. Better zero-shot reasoning with self-adaptive prompting. arXiv preprint arXiv:2305.14106. [find](https://scholar.google.com/scholar?q=Xingchen+Wan%2C+Ruoxi+Sun%2C+Hanjun+Dai%2C+Sercan+O+Arik%2C+and+Tomas+Pfister%2E+2023%2E+Better+zero%2Dshot+reasoning+with+self%2Dadaptive+prompting%2E+arXiv+preprint+arXiv%3A2305%2E14106%2E)
122. Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018. [Glue: A multi-task benchmark and analysis platform for natural language understanding](https://md.research.elamur.ai/free/en/papers/wang-2018-glue-multi-task-benchmark-analysis/). In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 353–355.
123. Boshi Wang, Sewon Min, Xiang Deng, Jiaming Shen, You Wu, Luke Zettlemoyer, and Huan Sun. 2022a. [Towards understanding chain-of-thought prompting: An empirical study of what matters](https://md.research.elamur.ai/free/en/papers/wang-2023-understanding-chain-thought-prompting-empirical/). arXiv preprint arXiv:2212.10001.
124. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022b. [Text embeddings by weakly-supervised contrastive pre-training](https://md.research.elamur.ai/free/en/papers/wang-2022-text-embeddings-weakly-supervised-contrastive/).
125. Liang Wang, Nan Yang, and Furu Wei. 2023a. [Learning to retrieve in-context examples for large language models](https://md.research.elamur.ai/free/en/papers/wang-2024-learning-retrieve-context-examples-large/). arXiv preprint arXiv:2307.07164.
126. Xinyi Wang, Wanrong Zhu, and William Yang Wang. 2023b. [Large language models are implicitly topic models: Explaining and finding good demonstrations for in-context learning](https://md.research.elamur.ai/free/en/papers/wang-2023-large-language-models-latent-variable/). arXiv preprint arXiv:2301.11916.
127. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022c. [Self-consistency improves chain of thought reasoning in language models](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/). In The Eleventh International Conference on Learning Representations.
128. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022. [Chain of thought prompting elicits reasoning in large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). arXiv preprint arXiv:2201.11903.
129. Orion Weller, Marc Marone, Nathaniel Weir, Dawn Lawrie, Daniel Khashabi, and Benjamin Van Durme. 2023. [" according to..." prompting language models improves quoting from pre-training data](https://md.research.elamur.ai/free/en/papers/weller-2024-according-prompting-language-models-improves/). arXiv preprint arXiv:2305.13252.
130. Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. [A broad-coverage challenge corpus for sentence understanding through inference](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/). In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Pa…
131. Tomer Wolfson, Mor Geva, Ankit Gupta, Matt Gardner, Yoav Goldberg, Daniel Deutch, and Jonathan Berant. 2020. [Break it down: A question understanding benchmark](https://md.research.elamur.ai/free/en/papers/wolfson-2020-break-it-down-question-understanding/). Transactions of the Association for Computational Linguistics, 8:183–198.
132. Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2021. [An explanation of in-context learning as implicit bayesian inference](https://md.research.elamur.ai/free/en/papers/xie-2021-explanation-context-learning-implicit-bayesian/). In International Conference on Learning Representations.
133. Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2020. [mt5: A massively multilingual pre-trained text-to-text transformer](https://md.research.elamur.ai/free/en/papers/xue-2021-mt5-massively-multilingual-pre-trained/). CoRR, abs/2010.11934.
134. Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen. 2023. [Large language models as optimizers](https://md.research.elamur.ai/free/en/papers/yang-2023-large-language-models-optimizers/). arXiv preprint arXiv:2309.03409.
135. Xu Yang, Yongliang Wu, Mingzhuo Yang, Haokun Chen, and Xin Geng. 2024. Exploring diverse in-context configurations for image captioning. Advances in Neural Information Processing Systems, 36. [find](https://scholar.google.com/scholar?q=Xu+Yang%2C+Yongliang+Wu%2C+Mingzhuo+Yang%2C+Haokun+Chen%2C+and+Xin+Geng%2E+2024%2E+Exploring+diverse+in%2Dcontext+configurations+for+image+captioning%2E+Advances+in+Neural+Information+Processing+Systems%2C+36%2E)
136. Yinfei Yang, Daniel Cer, Amin Ahmad, Mandy Guo, Jax Law, Noah Constant, Gustavo Hernández Ábrego, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, and Ray Kurzweil. 2019. [Multilingual universal sentence encoder for semantic retrieval](https://md.research.elamur.ai/free/en/papers/yang-2020-multilingual-universal-sentence-encoder-semantic/). CoRR, abs/1907.04307.
137. Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Richard James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, and Wen-Tau Yih. 2023. Retrieval-augmented multimodal language modeling. In International Conference on Machine Learning, pages 39755–39769. PMLR. [find](https://scholar.google.com/scholar?q=Michihiro+Yasunaga%2C+Armen+Aghajanyan%2C+Weijia+Shi%2C+Richard+James%2C+Jure+Leskovec%2C+Percy+Liang%2C+Mike+Lewis%2C+Luke+Zettlemoyer%2C+and+Wen%2DTau+Yih%2E+2023%2E+Retrieval%2Daugmented+multimodal+language+modeling%2E+In+I)
138. Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, Tao Yu, and Lingpeng Kong. 2023a. Compositional exemplars for in-context learning. arXiv preprint arXiv:2302.05698. [find](https://scholar.google.com/scholar?q=Jiacheng+Ye%2C+Zhiyong+Wu%2C+Jiangtao+Feng%2C+Tao+Yu%2C+and+Lingpeng+Kong%2E+2023a%2E+Compositional+exemplars+for+in%2Dcontext+learning%2E+arXiv+preprint+arXiv%3A2302%2E05698%2E)
139. Seonghyeon Ye, Hyeonbin Hwang, Sohee Yang, Hyeongu Yun, Yireun Kim, and Minjoon Seo. 2023b. In-context instruction learning. arXiv preprint arXiv:2302.14691. — [arXiv:2302.14691](https://arxiv.org/abs/2302.14691) [find](https://scholar.google.com/scholar?q=Seonghyeon+Ye%2C+Hyeonbin+Hwang%2C+Sohee+Yang%2C+Hyeongu+Yun%2C+Yireun+Kim%2C+and+Minjoon+Seo%2E+2023b%2E+In%2Dcontext+instruction+learning%2E+arXiv+preprint+arXiv%3A2302%2E14691%2E+%E2%80%94+%5BarXiv%3A2302%2E14691%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs)
140. Xue Ying. 2019. An overview of overfitting and its solutions. In Journal of physics: Conference series, volume 1168, page 022022. IOP Publishing. [find](https://scholar.google.com/scholar?q=Xue+Ying%2E+2019%2E+An+overview+of+overfitting+and+its+solutions%2E+In+Journal+of+physics%3A+Conference+series%2C+volume+1168%2C+page+022022%2E+IOP+Publishing%2E)
141. Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju, Soumya Sanyal, Chenguang Zhu, Michael Zeng, and Meng Jiang. 2023. [Generate rather than retrieve: Large language models are strong context generators](https://md.research.elamur.ai/free/en/papers/yu-2022-generate-rather-than-retrieve-large/). In The Eleventh International Conference on Learning Representations.
142. Quan Yuan, Mehran Kazemi, Xin Xu, Isaac Noble, Vaiva Imbrasaite, and Deepak Ramachandran. 2023. Tasklama: probing the complex task understanding of language models. arXiv preprint arXiv:2308.15299. — [arXiv:2308.15299](https://arxiv.org/abs/2308.15299) [find](https://scholar.google.com/scholar?q=Quan+Yuan%2C+Mehran+Kazemi%2C+Xin+Xu%2C+Isaac+Noble%2C+Vaiva+Imbrasaite%2C+and+Deepak+Ramachandran%2E+2023%2E+Tasklama%3A+probing+the+complex+task+understanding+of+language+models%2E+arXiv+preprint+arXiv%3A2308%2E15299%2E+%E2%80%94+)
143. John M Zelle and Raymond J Mooney. 1996. [Learning to parse database queries using inductive logic programming](https://md.research.elamur.ai/free/en/papers/zelle-1996-learning-parse-database-queries-using/). In Proceedings of the national conference on artificial intelligence, pages 1050–1055.
144. Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019. Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4791–4800. [find](https://scholar.google.com/scholar?q=Rowan+Zellers%2C+Ari+Holtzman%2C+Yonatan+Bisk%2C+Ali+Farhadi%2C+and+Yejin+Choi%2E+2019%2E+Hellaswag%3A+Can+a+machine+really+finish+your+sentence%3F+In+Proceedings+of+the+57th+Annual+Meeting+of+the+Association+for+Com)
145. Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, et al. 2023. [Instruction tuning for large language models: A survey](https://md.research.elamur.ai/free/en/papers/zhang-2023-instruction-tuning-large-language-models/). arXiv preprint arXiv:2308.10792.
146. Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. [Character-level convolutional networks for text classification](https://md.research.elamur.ai/free/en/papers/zhang-2015-character-level-convolutional-networks-text/). Advances in neural information processing systems, 28.
147. Yiming Zhang, Shi Feng, and Chenhao Tan. 2022a. [Active example selection for in-context learning](https://md.research.elamur.ai/free/en/papers/zhang-2022-active-example-selection-context-learning/). In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9134–9148.
148. Yuan Zhang, Jason Baldridge, and Luheng He. 2019. [PAWS: Paraphrase Adversaries from Word Scrambling](https://md.research.elamur.ai/free/en/papers/zhang-2019-paws-paraphrase-adversaries-word-scrambling/). In Proc. of NAACL.
149. Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2022b. [Automatic chain of thought prompting in large language models](https://md.research.elamur.ai/free/en/papers/zhang-2022-automatic-chain-thought-prompting-large/). In The Eleventh International Conference on Learning Representations.
150. Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023. [A survey of large language models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/). arXiv preprint arXiv:2303.18223.
151. Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021. [Calibrate before use: Improving few-shot performance of language models](https://md.research.elamur.ai/free/en/papers/zhao-2021-calibrate-before-use-improving-few/). In International Conference on Machine Learning, pages 12697–12706. PMLR.

*Updated 31 October 2026*