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Retrieval-augmented generation has gained significant attention due to its ability to integrate relevant external knowledge, enhancing the accuracy and reliability of the LLMs' responses.
Language Models are Few-Shot Learners
Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D. M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; and Amodei, D. 2020 · 2005
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The probabilistic relevance framework: BM25 and beyond
Robertson, S.; Zaragoza, H.; et al. 2009 · 2009
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Adversarial inquisitions: Rethinking the search for the truth
Findley, K. A. 2011 · 2011
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HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Yang, Z.; Qi, P.; Zhang, S.; Bengio, Y.; Cohen, W.; Salakhutdinov, R.; and Manning, C. D. 2018 · 2018
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REALM: retrieval-augmented language model pre-training
Guu, K.; Lee, K.; Tung, Z.; Pasupat, P.; and Chang, M.-W. 2020 · 2020
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Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
Ho, X.; Duong Nguyen, A.-K.; Sugawara, S.; and Aizawa, A. 2020 · 2020
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Dense Passage Retrieval for Open-Domain Question Answering
Karpukhin, V.; Oguz, B.; Min, S.; Lewis, P.; Wu, L.; Edunov, S.; Chen, D.; and Yih, W.-t. 2020 · 2020
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Retrieval-augmented generation for knowledge-intensive NLP tasks
Lewis, P.; Perez, E.; Piktus, A.; Petroni, F.; Karpukhin, V.; Goyal, N.; Küttler, H.; Lewis, M.; Yih, W.-t.; Rocktäschel, T.; Riedel, S.; and Kiela, D. 2020 · 2020
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Efficient Nearest Neighbor Language Models
He, J.; Neubig, G.; and Berg-Kirkpatrick, T. 2021 · 2021
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Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
Izacard, G.; and Grave, E. 2021 · 2021
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Generation-Augmented Retrieval for Open-Domain Question Answering
Mao, Y.; He, P.; Liu, X.; Shen, Y.; Gao, J.; Han, J.; and Chen, W. 2021 · 2021
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Beyond reasonable doubt: how to think like an expert detective
Fahsing, I. 2022 · 2022
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Rethinking with retrieval: Faithful large language model inference
He, H.; Zhang, H.; and Roth, D. 2022 · 2022
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Large language models can self-improve
Huang, J.; Gu, S. S.; Hou, L.; Wu, Y.; Wang, X.; Yu, H.; and Han, J. 2022 · 2022
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Factuality enhanced language models for open-ended text generation
Lee, N.; Ping, W.; Xu, P.; Patwary, M.; Fung, P. N.; Shoeybi, M.; and Catanzaro, B. 2022 · 2022
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Chatgpt: Optimizing language models for dialogue
OpenAI. 2022 · 2022
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Wang, X.; Wei, J.; Schuurmans, D.; Le, Q.; Chi, E.; Narang, S.; Chowdhery, A.; and Zhou, D. 2022 · 2022
Cited alongside, same era.
Evaluating hallucinations in chinese large language models
Cheng, Q.; Sun, T.; Zhang, W.; Wang, S.; Liu, X.; Zhang, M.; He, J.; Huang, M.; Yin, Z.; Chen, K.; et al. 2023 · 2023
Cited alongside, same era.
Epistemic norms on evidence-gathering
Flores, C.; and Woodard, E. 2023 · 2023
Cited alongside, same era.
RARR: Researching and Revising What Language Models Say, Using Language Models
Gao, L.; Dai, Z.; Pasupat, P.; Chen, A.; Chaganty, A. T.; Fan, Y.; Zhao, V.; Lao, N.; Lee, H.; Juan, D.-C.; and Guu, K. 2023 · 2023
Cited alongside, same era.
Cognitive mirage: A review of hallucinations in large language models
Ye, H.; Liu, T.; Zhang, A.; Hua, W.; and Jia, W. 2023 · 2023
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Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
Yu, W.; Zhang, H.; Pan, X.; Ma, K.; Wang, H.; and Yu, D. 2023 · 2023
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Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Asai, A.; Wu, Z.; Wang, Y.; Sil, A.; and Hajishirzi, H. 2024 · 2024
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Benchmarking large language models in retrieval-augmented generation
Chen, J.; Lin, H.; Han, X.; and Sun, L. 2024 · 2024
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Over-Reasoning and Redundant Calculation of Large Language Models
Chiang, C.-H.; and Lee, H.-y. 2024 · 2024
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SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
Manakul, P.; Liusie, A.; and Gales, M. 2023 · 2023
Cited alongside, same era.
Measuring and Narrowing the Compositionality Gap in Language Models
Press, O.; Zhang, M.; Min, S.; Schmidt, L.; Smith, N.; and Lewis, M. 2023 · 2023
Cited alongside, same era.
In-context retrieval-augmented language models
Ram, O.; Levine, Y.; Dalmedigos, I.; Muhlgay, D.; Shashua, A.; Leyton-Brown, K.; and Shoham, Y. 2023 · 2023
Cited alongside, same era.
Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
Shao, Z.; Gong, Y.; Shen, Y.; Huang, M.; Duan, N.; and Chen, W. 2023 · 2023
Cited alongside, same era.
Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering
Siriwardhana, S.; Weerasekera, R.; Wen, E.; Kaluarachchi, T.; Rana, R.; and Nanayakkara, S. 2023 · 2023
Cited alongside, same era.
Fine-tuning language models for factuality
Tian, K.; Mitchell, E.; Yao, H.; Manning, C. D.; and Finn, C. 2023 · 2023
Cited alongside, same era.
Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
Trivedi, H.; Balasubramanian, N.; Khot, T.; and Sabharwal, A. 2023 · 2023
Cited alongside, same era.
Detecting hallucinations in large language models using semantic entropy
Farquhar, S.; Kossen, J.; Kuhn, L.; and Gal, Y. 2024 · 2024
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RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback
Liu, Y.; Peng, X.; Zhang, X.; Liu, W.; Yin, J.; Cao, J.; and Du, T. 2024 · 2024
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Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation
Mündler, N.; He, J.; Jenko, S.; and Vechev, M. 2024 · 2024
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REPLUG: Retrieval-Augmented Black-Box Language Models
Shi, W.; Min, S.; Yasunaga, M.; Seo, M.; James, R.; Lewis, M.; Zettlemoyer, L.; and Yih, W.-t. 2024 · 2024
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ClashEval: Quantifying the tug-of-war between an LLM’s internal prior and external evidence
Wu, K.; Wu, E.; and Zou, J. 2024 · 2024
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How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?
Wu, S.; Xie, J.; Chen, J.; Zhu, T.; Zhang, K.; and Xiao, Y. 2024 · 2024
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Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks
Xu, S.; Pang, L.; Shen, H.; Cheng, X.; and Chua, T.-S. 2024 · 2024
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Corrective retrieval augmented generation
Yan, S.-Q.; Gu, J.-C.; Zhu, Y.; and Ling, Z.-H. 2024 · 2024
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Metacognitive Retrieval-Augmented Large Language Models
Zhou, Y.; Liu, Z.; Jin, J.; Nie, J.-Y.; and Dou, Z. 2024 · 2024
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