---
title: "Who cites Retrieval-Augmented — 3,230 citing papers"
description: "3,230 papers of the Elamur Research library cite «Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks», found in their…"
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# Who cites «Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks»

3,230 papers of our library cite it (of the 149,439 whose bibliographies we read) · 578 with a page here, the most cited first · page 1 of 6 · 1–100 · [← back to «Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks»](https://md.research.elamur.ai/free/en/papers/lewis-2020-retrieval-augmented-generation-knowledge-intensive/)

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1. [**Language Models are Few-Shot Learners arXiv:2005.14165**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 14,178 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
2. **[Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,423 citations*
3. **[Dense Passage Retrieval for Open-Domain Question Answering](https://md.research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Vladimir Karpukhin, Barlas Oguz, Sewon Min, et al.*2020 · 5,421 citations*
4. **[A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,231 citations*
5. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
6. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
7. [**ReAct: Synergizing Reasoning and Acting in Language Models arXiv:2210.03629**Shunyu Yao, Jeffrey Zhao, Dian Yu, et al.*2022 · 2,591 citations*](https://md.research.elamur.ai/free/en/papers/yao-2022-react-synergizing-reasoning-acting-language/)
8. [**Sparks of Artificial General Intelligence: Early experiments with GPT-4 arXiv:2303.12712**Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, et al.*2023 · 2,439 citations*](https://md.research.elamur.ai/free/en/papers/bubeck-2023-sparks-artificial-general-intelligence-early/)
9. **[TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://md.research.elamur.ai/free/en/papers/lin-2022-truthfulqa-measuring-how-models-mimic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Stephanie Lin, Jacob Hilton, Owain Evans*2022 · 2,088 citations*
10. **[Training Compute-Optimal Large Language Models](https://md.research.elamur.ai/free/en/papers/hoffmann-2022-training-compute-optimal-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, et al.*2022 · 2,032 citations*
11. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,756 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
12. [**OPT: Open Pre-trained Transformer Language Models arXiv:2205.01068**Susan Zhang, Stephen Roller, Naman Goyal, et al.*2022 · 1,755 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2022-opt-open-pre-trained-transformer/)
13. **[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/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Gautier Izacard, Edouard Grave*2021 · 1,692 citations*
14. [**Retrieval-Augmented Generation for Large Language Models: A Survey arXiv:2312.10997**Yunfan Gao, Yun Xiong, Xinyu Gao, et al.*2023 · 1,663 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/)
15. **[Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://md.research.elamur.ai/free/en/papers/pan-2023-unifying-large-language-models-knowledge/) [IEEE Transactions on Knowledge and Data Engineering](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-knowledge-and-data-engineering/)**Shirui Pan, Linhao Luo, Yufei Wang, et al.*2023 · 1,551 citations*
16. [**LaMDA: Language Models for Dialog Applications arXiv:2201.08239**Romal Thoppilan, Daniel De Freitas, Jamie Hall, et al.*2022 · 1,344 citations*](https://md.research.elamur.ai/free/en/papers/thoppilan-2022-lamda-language-models-dialog-applications/)
17. [**What Makes Good In-Context Examples for GPT-3? Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures**Jiachang Liu, Dinghan Shen, Yizhe Zhang, et al.*2022 · 1,256 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-what-makes-good-context-examples/)
18. **[Big Bird: Transformers for Longer Sequences](https://md.research.elamur.ai/free/en/papers/zaheer-2020-big-bird-transformers-longer-sequences/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Manzil Zaheer, Guru Guruganesh, Avinava Dubey, et al.*2020 · 1,023 citations*
19. **[A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/fan-2024-survey-rag-meeting-llms-towards/) [ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/)**Wenqi Fan; Yujuan Ding; Liangbo Ning; Shijie Wang; Hengyun Li; Dawei Yin; Tat-Seng Chua;, Qing Li*2024 · 1,010 citations*
20. **[Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Yue Zhang, Yafu Li, Leyang Cui, et al.*2023 · 977 citations*
21. **[C-Pack: Packed Resources For General Chinese Embeddings](https://md.research.elamur.ai/free/en/papers/xiao-2023-c-pack-packed-resources/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Shitao Xiao, Zheng Liu, Peitian Zhang, et al.*2023 · 922 citations*
22. **[Active Retrieval Augmented Generation](https://md.research.elamur.ai/free/en/papers/jiang-2023-active-retrieval-augmented-generation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhengbao Jiang, Frank Xu, Luyu Gao, et al.*2023 · 872 citations*
23. **[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 (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Ori Ram, Yoav Levine, Itay Dalmedigos, et al.*2023 · 793 citations*
24. **[Learning To Retrieve Prompts for In-Context Learning](https://md.research.elamur.ai/free/en/papers/rubin-2021-learning-retrieve-prompts-context-learning/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ohad Rubin, Jonathan Herzig, Jonathan Berant*2021 · 724 citations*
25. [**WebGPT: Browser-assisted question-answering with human feedback arXiv:2112.09332**Reiichiro Nakano, Jacob Hilton, Suchir Balaji, et al.*2021 · 721 citations*](https://md.research.elamur.ai/free/en/papers/nakano-2021-webgpt-browser-assisted-question-answering/)
26. **[Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://md.research.elamur.ai/free/en/papers/trivedi-2022-interleaving-retrieval-chain-thought-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, et al.*2022 · 716 citations*
27. [**Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval arXiv:2007.00808**Lee Xiong, Chenyan Xiong, Ye Li, et al.*2020 · 688 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2020-approximate-nearest-neighbor-negative-contrastive/)
28. **[KILT: a Benchmark for Knowledge Intensive Language Tasks](https://md.research.elamur.ai/free/en/papers/petroni-2020-kilt-benchmark-knowledge-intensive-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Fabio Petroni, Aleksandra Piktus, Angela Fan, et al.*2020 · 678 citations*
29. [**Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection arXiv:2310.11511**Akari Asai, Zeqiu Wu, Yizhong Wang, et al.*2023 · 677 citations*](https://md.research.elamur.ai/free/en/papers/asai-2023-self-rag-learning-retrieve-generate/)
30. [**Atlas: Few-shot Learning with Retrieval Augmented Language Models arXiv:2208.03299**Gautier Izacard, Patrick Lewis, Maria Lomeli, et al.*2022 · 673 citations*](https://md.research.elamur.ai/free/en/papers/izacard-2022-atlas-few-shot-learning-retrieval/)
31. **[HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2023-halueval-large-scale-hallucination-evaluation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, et al.*2023 · 651 citations*
32. **[A Survey on Automated Fact-Checking](https://md.research.elamur.ai/free/en/papers/guo-2022-survey-automated-fact-checking/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Zhijiang Guo, Michael Schlichtkrull, Andreas Vlachos*2022 · 647 citations*
33. **[Benchmarking Large Language Models in Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/chen-2023-benchmarking-large-language-models-retrieval/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Jiawei Chen, Hongyu Lin, Xianpei Han, et al.*2023 · 629 citations*
34. [**AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation arXiv:2308.08155**Qingyun Wu, Gagan Bansal, Jieyu Zhang, et al.*2023 · 606 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-autogen-enabling-next-gen-llm/)
35. **[RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering](https://md.research.elamur.ai/free/en/papers/qu-2021-rocketqa-optimized-training-approach-dense/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yingqi Qu, Yuchen Ding, Jing Liu, et al.*2021 · 603 citations*
36. **[Learning How to Ask: Querying LMs with Mixtures of Soft Prompts](https://md.research.elamur.ai/free/en/papers/qin-2021-learning-ask-querying-lms-mixtures/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Guanghui Qin, Jason Eisner*2021 · 573 citations*
37. [**Galactica: A Large Language Model for Science arXiv:2211.09085**Ross Taylor, Marcin Kardas, Guillem Cucurull, et al.*2022 · 570 citations*](https://md.research.elamur.ai/free/en/papers/taylor-2022-galactica-large-language-model-science/)
38. [**Unleashing the potential of prompt engineering for large language models arXiv:2310.14735 · Patterns 6(6) 101,260 (2025)**Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.*2023 · 536 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-unleashing-potential-prompt-engineering-large/)
39. [**REPLUG: Retrieval-Augmented Black-Box Language Models arXiv:2301.12652**Weijia Shi, Sewon Min, Michihiro Yasunaga, et al.*2023 · 492 citations*](https://md.research.elamur.ai/free/en/papers/shi-2023-replug-retrieval-augmented-black-box/)
40. **[LinkBERT: Pretraining Language Models with Document Links](https://md.research.elamur.ai/free/en/papers/yasunaga-2022-linkbert-pretraining-language-models-document/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Michihiro Yasunaga, Jure Leskovec, Percy Liang*2022 · 458 citations*
41. **[Improving Text Embeddings with Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-improving-text-embeddings-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2024 · 437 citations*
42. **[Benchmarking Retrieval-Augmented Generation for Medicine](https://md.research.elamur.ai/free/en/papers/xiong-2024-benchmarking-retrieval-augmented-generation-medicine/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Guangzhi Xiong, Qiao Jin, Zhiyong Lu, et al.*2024 · 430 citations*
43. **[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 (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, et al.*2023 · 403 citations*
44. [**Retrieval Augmentation Reduces Hallucination in Conversation arXiv:2104.07567**Kurt Shuster, Spencer Poff, Moya Chen, et al.*2021 · 392 citations*](https://md.research.elamur.ai/free/en/papers/shuster-2021-retrieval-augmentation-reduces-hallucination-conversation/)
45. **[Query Rewriting in Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/ma-2023-query-rewriting-retrieval-augmented-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xinbei Ma, Yeyun Gong, Pengcheng He, et al.*2023 · 384 citations*
46. **[Tool Learning with Foundation Models](https://md.research.elamur.ai/free/en/papers/qin-2023-tool-learning-foundation-models/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Yujia Qin, Shengding Hu, Yankai Lin, et al.*2023 · 383 citations*
47. [**A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications arXiv:2402.07927**Pranab Sahoo; Ayush Kumar Singh; Sriparna Saha; Vinija Jain; Samrat Mondal;, Aman Chadha*2024 · 375 citations*](https://md.research.elamur.ai/free/en/papers/sahoo-2024-systematic-survey-prompt-engineering-large/)
48. [**Augmented Language Models: a Survey arXiv:2302.07842**Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al.*2023 · 369 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-augmented-language-models-survey/)
49. [**Datasets: A Community Library for Natural Language Processing arXiv:2109.02846**Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, et al.*2021 · 368 citations*](https://md.research.elamur.ai/free/en/papers/lhoest-2021-datasets-community-library-natural-language/)
50. [**A Survey on Large Language Models for Code Generation arXiv:2406.00515**Juyong Jiang, Fan Wang, Jiasi Shen, et al.*2024 · 367 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2024-survey-large-language-models-code/)
51. [**Large Language Models Struggle to Learn Long-Tail Knowledge arXiv:2211.08411**Nikhil Kandpal, Haikang Deng, Adam Roberts, et al.*2022 · 366 citations*](https://md.research.elamur.ai/free/en/papers/kandpal-2022-large-language-models-struggle-learn/)
52. [**Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback arXiv:2302.12813**Baolin Peng, Michel Galley, Pengcheng He, et al.*2023 · 363 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-check-facts-try-again-improving/)
53. [**Improving alignment of dialogue agents via targeted human judgements arXiv:2209.14375**Amelia Glaese, Nat McAleese, Maja Trębacz, et al.*2022 · 362 citations*](https://md.research.elamur.ai/free/en/papers/glaese-2022-improving-alignment-dialogue-agents-targeted/)
54. **[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/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Luyu Gao, Zhuyun Dai, Panupong Pasupat, et al.*2023 · 359 citations*
55. [**Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference arXiv:2412.13663**Benjamin Warner, Antoine Chaffin, Benjamin Clavié, et al.*2024 · 358 citations*](https://md.research.elamur.ai/free/en/papers/warner-2024-smarter-better-faster-longer-modern/)
56. **[Towards Mitigating LLM Hallucination via Self Reflection](https://md.research.elamur.ai/free/en/papers/ji-2023-mitigating-llm-hallucination-self-reflection/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ziwei Ji, Tiezheng Yu, Yan Xu, et al.*2023 · 354 citations*
57. **[Internet-Augmented Dialogue Generation](https://md.research.elamur.ai/free/en/papers/komeili-2022-internet-augmented-dialogue-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mojtaba Komeili, Kurt Shuster, Jason Weston*2022 · 354 citations*
58. **[Large Language Models for Information Retrieval: A Survey](https://md.research.elamur.ai/free/en/papers/zhu-2023-large-language-models-information-retrieval/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Yutao Zhu; Huaying Yuan; Shuting Wang; Jiongnan Liu; Wenhan Liu; Chenlong Deng; Haonan Chen; Zheng Liu; Zhicheng Dou;, Ji-Rong Wen*2023 · 339 citations*
59. [**CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing arXiv:2305.11738**Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.*2023 · 326 citations*](https://md.research.elamur.ai/free/en/papers/gou-2023-critic-large-language-models-self/)
60. [**Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning arXiv:2503.09516**Bowen Jin, Hansi Zeng, Zhenrui Yue, et al.*2025 · 322 citations*](https://md.research.elamur.ai/free/en/papers/jin-2025-search-r1-training-llms-reason/)
61. [**Hallucination is Inevitable: An Innate Limitation of Large Language Models arXiv:2401.11817**Ziwei Xu, Sanjay Jain, Mohan Kankanhalli*2024 · 312 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-hallucination-inevitable-innate-limitation-large/)
62. **[The Power of Noise: Redefining Retrieval for RAG Systems](https://md.research.elamur.ai/free/en/papers/cuconasu-2024-power-noise-redefining-retrieval-rag/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Florin Cuconasu, Giovanni Trappolini, Federico Siciliano, et al.*2024 · 312 citations*
63. [**The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) arXiv:2309.17421**Zhengyuan Yang, Linjie Li, Kevin Lin, et al.*2023 · 311 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-dawn-lmms-preliminary-explorations-gpt/)
64. [**Retrieval-Augmented Generation for AI-Generated Content: A Survey arXiv:2402.19473**Penghao Zhao, Hailin Zhang, Qinhan Yu, et al.*2024 · 301 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2024-retrieval-augmented-generation-ai-generated/)
65. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
66. **[Entity-Based Knowledge Conflicts in Question Answering](https://md.research.elamur.ai/free/en/papers/longpre-2021-entity-based-knowledge-conflicts-question/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shayne Longpre, Kartik Perisetla, Anthony Chen, et al.*2021 · 298 citations*
67. **[Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://md.research.elamur.ai/free/en/papers/schuster-2021-get-your-vitamin-c-robust/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Tal Schuster, Adam Fisch, Regina Barzilay*2021 · 290 citations*
68. [**A Review of Large Language Models and Autonomous Agents in Chemistry arXiv:2407.01603**Mayk Caldas Ramos, Christopher J. Collison, Andrew D. White*2024 · 288 citations*](https://md.research.elamur.ai/free/en/papers/ramos-2024-review-large-language-models-autonomous/)
69. [**Large Language Model based Multi-Agents: A Survey of Progress and Challenges arXiv:2402.01680**Taicheng Guo, Xiuying Chen, Yaqi Wang, et al.*2024 · 288 citations*](https://md.research.elamur.ai/free/en/papers/guo-2024-large-language-model-based/)
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