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Retrieval-augmented generation (RAG) has emerged as a promising solution to mitigate the limitations of large language models (LLMs), such as hallucinations and outdated information.
Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, S. Riedel, and D. Kiela · 2020
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Datasheets for datasets
T. Gebru, J. Morgenstern, B. Vecchione, J. W. Vaughan, H. Wallach, H. D. Iii, and K. Crawford · 2021
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Retrieval-augmented generation for knowledge-intensive nlp tasks, 2021
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. tau Yih, T. Rocktäschel, S. Riedel, and D. Kiela · 2021
Earlier work this paper cites.
Entity-based knowledge conflicts in question answering
S. Longpre, K. Perisetla, A. Chen, N. Ramesh, C. DuBois, and S. Singh · 2021
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Truthfulqa: Measuring how models mimic human falsehoods
S. Lin, J. Hilton, and O. Evans · 2022
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Felm: Benchmarking factuality evaluation of large language models
S. Chen, Y. Zhao, J. Zhang, I.-C. Chern, S. Gao, P. Liu, and J. He · 2023
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Evaluating hallucinations in chinese large language models, 2023
Q. Cheng, T. Sun, W. Zhang, S. Wang, X. Liu, M. Zhang, J. He, M. Huang, Z. Yin, K. Chen, and X. Qiu · 2023
Cited alongside, same era.
A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions, 2023
L. Huang, W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wang, Q. Chen, W. Peng, X. Feng, B. Qin, and T. Liu · 2023
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Halueval: A large-scale hallucination evaluation benchmark for large language models
J. Li, X. Cheng, W. X. Zhao, J.-Y. Nie, and J.-R. Wen · 2023
Cited alongside, same era.
FActScore: Fine-grained atomic evaluation of factual precision in long form text generation
S. Min, K. Krishna, X. Lyu, M. Lewis, W.-t. Yih, P. Koh, M. Iyyer, L. Zettlemoyer, and H. Hajishirzi · 2023
Resolving knowledge conflicts in large language models, 2023
Y. Wang, S. Feng, H. Wang, W. Shi, V. Balachandran, T. He, and Y. Tsvetkov · 2023
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Tug-of-war between knowledge: Exploring and resolving knowledge conflicts in retrieval-augmented language models
Z. Jin, P. Cao, Y. Chen, K. Liu, X. Jiang, J. Xu, L. Qiuxia, and J. Zhao · 2024
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Contradoc: Understanding self-contradictions in documents with large language models, 2024
J. Li, V. Raheja, and D. Kumar · 2024
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Long-form factuality in large language models, 2024
J. Wei, C. Yang, X. Song, Y. Lu, N. Hu, J. Huang, D. Tran, D. Peng, R. Liu, D. Huang, C. Du, and Q. V. Le · 2024
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Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts
J. Xie, K. Zhang, J. Chen, R. Lou, and Y. Su · 2024
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Freshllms: Refreshing large language models with search engine augmentation, 2023
T. Vu, M. Iyyer, X. Wang, N. Constant, J. Wei, J. Wei, C. Tar, Y.-H. Sung, D. Zhou, Q. Le, and T. Luong · 2023
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R. Xu, Z. Qi, C. Wang, H. Wang, Y. Zhang, and W. Xu · 2024
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