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Retrieval-Augmented Generation (RAG) is a promising approach for mitigating the hallucination of large language models (LLMs).
On Faithfulness and Factuality in Abstractive Summarization
Maynez, J.; Narayan, S.; Bohnet, B.; and McDonald, R. 2020 · 1919
Earlier work this paper cites.
Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory
Cai, D.; Wang, Y.; Bi, W.; Tu, Z.; Liu, X.; Lam, W.; and Shi, S. 2019a · 2019
Earlier work this paper cites.
Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework
Cai, D.; Wang, Y.; Bi, W.; Tu, Z.; Liu, X.; and Shi, S. 2019b · 2019
Earlier work this paper cites.
Factual Error Correction for Abstractive Summarization Models
Cao, M.; Dong, Y.; Wu, J.; and Cheung, J. C. K. 2020 · 2020
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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
Earlier work this paper cites.
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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Measuring Massive Multitask Language Understanding
Hendrycks, D.; Burns, C.; Basart, S.; Zou, A.; Mazeika, M.; Song, D.; and Steinhardt, J. 2021 · 2021
Earlier work this paper cites.
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
Izacard, G.; and Grave, E. 2021 · 2021
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The Curious Case of Hallucinations in Neural Machine Translation
Raunak, V.; Menezes, A.; and Junczys-Dowmunt, M. 2021 · 2021
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Improving language models by retrieving from trillions of tokens
Borgeaud, S.; Mensch, A.; Hoffmann, J.; Cai, T.; Rutherford, E.; Millican, K.; van den Driessche, G.; Lespiau, J.-B.; Damoc, B.; Clark, A.; de Las Casas, D.; Guy, A.; Menick, J.; Ring, R.; Hennigan, T.; Huang, S.; Maggiore, L.; Jones, C.; Cassirer, A.; Brock, A.; Paganini, M.; Irving, G.; Vinyals, O.; Osindero, S.; Simonyan, K.; Rae, J. W.; Elsen, E.; and Sifre, L. 2022 · 2022
Earlier work this paper cites.
Rethinking with Retrieval: Faithful Large Language Model Inference
He, H.; Zhang, H.; and Roth, D. 2022 · 2022
Earlier work this paper cites.
Atlas: Few-shot Learning with Retrieval Augmented Language Models
Izacard, G.; Lewis, P.; Lomeli, M.; Hosseini, L.; Petroni, F.; Schick, T.; Dwivedi-Yu, J.; Joulin, A.; Riedel, S.; and Grave, E. 2022 · 2022
Earlier work this paper cites.
Chatgpt: Optimizing language models for dialogue
OpenAI. 2022 · 2022
Cited alongside, same era.
Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering
Adlakha, V.; BehnamGhader, P.; Lu, X. H.; Meade, N.; and Reddy, S. 2023 · 2023
Cited alongside, same era.
Bang, Y.; Cahyawijaya, S.; Lee, N.; Dai, W.; Su, D.; Wilie, B.; Lovenia, H.; Ji, Z.; Yu, T.; Chung, W.; Do, Q. V.; Xu, Y.; and Fung, P. 2023 · 2023
Cited alongside, same era.
A Drop of Ink Makes a Million Think: The Spread of False Information in Large Language Models
Bian, N.; Liu, P.; Han, X.; Lin, H.; Lu, Y.; He, B.; and Sun, L. 2023 · 2023
Cited alongside, same era.
Large Language Models with Controllable Working Memory
Li, D.; Rawat, A. S.; Zaheer, M.; Wang, X.; Lukasik, M.; Veit, A.; Yu, F.; and Kumar, S. 2023a · 2023
Closest in time.
Evaluating Verifiability in Generative Search Engines
Liu, N. F.; Zhang, T.; and Liang, P. 2023 · 2023
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Peng, B.; Galley, M.; He, P.; Cheng, H.; Xie, Y.; Hu, Y.; Huang, Q.; Liden, L.; Yu, Z.; Chen, W.; and Gao, J. 2023 · 2023
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ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
Qin, Y.; Liang, S.; Ye, Y.; Zhu, K.; Yan, L.; Lu, Y.; Lin, Y.; Cong, X.; Tang, X.; Qian, B.; Zhao, S.; Tian, R.; Xie, R.; Zhou, J.; Gerstein, M.; Li, D.; Liu, Z.; and Sun, M. 2023 · 2023
Closest in time.
Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
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Chang, Y.; Wang, X.; Wang, J.; Wu, Y.; Yang, L.; Zhu, K.; Chen, H.; Yi, X.; Wang, C.; Wang, Y.; Ye, W.; Zhang, Y.; Chang, Y.; Yu, P. S.; Yang, Q.; and Xie, X. 2023 · 2023
Cited alongside, same era.
Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality
Chiang, W.-L.; Li, Z.; Lin, Z.; Sheng, Y.; Wu, Z.; Zhang, H.; Zheng, L.; Zhuang, S.; Zhuang, Y.; Gonzalez, J. E.; Stoica, I.; and Xing, E. P. 2023 · 2023
Cited alongside, same era.
ChatLaw: Open-Source Legal Large Language Model with Integrated External Knowledge Bases
Cui, J.; Li, Z.; Yan, Y.; Chen, B.; and Yuan, L. 2023 · 2023
Cited alongside, same era.
Compositional Semantic Parsing with Large Language Models
Drozdov, A.; Schärli, N.; Akyürek, E.; Scales, N.; Song, X.; Chen, X.; Bousquet, O.; and Zhou, D. 2023 · 2023
Cited alongside, same era.
Open LLM Leaderboard
Edward Beeching, N. H. S. H. N. L. N. R. O. S. L. T. T. W., Clémentine Fourrier. 2023 · 2023
Cited alongside, same era.
How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
Guo, B.; Zhang, X.; Wang, Z.; Jiang, M.; Nie, J.; Ding, Y.; Yue, J.; and Wu, Y. 2023 · 2023
Cited alongside, same era.
C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models
Huang, Y.; Bai, Y.; Zhu, Z.; Zhang, J.; Zhang, J.; Su, T.; Liu, J.; Lv, C.; Zhang, Y.; Lei, J.; Fu, Y.; Sun, M.; and He, J. 2023 · 2023
Cited alongside, same era.
Survey of Hallucination in Natural Language Generation
Ji, Z.; Lee, N.; Frieske, R.; Yu, T.; Su, D.; Xu, Y.; Ishii, E.; Bang, Y. J.; Madotto, A.; and Fung, P. 2023 · 2023
Cited alongside, same era.
Ren, R.; Wang, Y.; Qu, Y.; Zhao, W. X.; Liu, J.; Tian, H.; Wu, H.; Wen, J.-R.; and Wang, H. 2023 · 2023
Closest in time.
In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT
Shen, X.; Chen, Z.; Backes, M.; and Zhang, Y. 2023 · 2023
Closest in time.
REPLUG: Retrieval-Augmented Black-Box Language Models
Shi, W.; Min, S.; Yasunaga, M.; Seo, M.; James, R.; Lewis, M.; Zettlemoyer, L.; and tau Yih, W. 2023 · 2023
Closest in time.
Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
Trivedi, H.; Balasubramanian, N.; Khot, T.; and Sabharwal, A. 2023 · 2023
Closest in time.
BELLE: Bloom-Enhanced Large Language model Engine
Yunjie Ji, Y. G. Y. P. Q. N. B. M. X. L., Yong Deng. 2023 · 2023
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M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models
Zhang, W.; Aljunied, S. M.; Gao, C.; Chia, Y. K.; and Bing, L. 2023 · 2023
Closest in time.
AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models
Zhong, W.; Cui, R.; Guo, Y.; Liang, Y.; Lu, S.; Wang, Y.; Saied, A.; Chen, W.; and Duan, N. 2023 · 2023
Closest in time.