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Retrieval-Augmented Generation (RAG) is a crucial method for mitigating hallucinations in Large Language Models (LLMs) and integrating external knowledge into their responses.
Scaling Laws for Neural Language Models
Kaplan, J.; McCandlish, S.; Henighan, T.; Brown, T. B.; Chess, B.; Child, R.; Gray, S.; Radford, A.; Wu, J.; and Amodei, D. 2020 · 2001
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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.; et al. 2020 · 2005
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Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
Ho, X.; Nguyen, A.-K. D.; Sugawara, S.; and Aizawa, A. 2020 · 2011
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TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M.; Choi, E.; Weld, D.; and Zettlemoyer, L. 2017 · 2017
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HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Yang, Z.; Qi, P.; Zhang, S.; Bengio, Y.; Cohen, W. W.; Salakhutdinov, R.; and Manning, C. D. 2018 · 2018
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Natural Questions: A Benchmark for Question Answering Research
Kwiatkowski, T.; Palomaki, J.; Redfield, O.; Collins, M.; Parikh, A.; Alberti, C.; Epstein, D.; Polosukhin, I.; Devlin, J.; Lee, K.; Toutanova, K.; Jones, L.; Kelcey, M.; Chang, M.-W.; Dai, A. M.; Uszkoreit, J.; Le, Q.; and Petrov, S. 2019 · 2019
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Retrieval-based neural source code summarization
Zhang, J.; Wang, X.; Zhang, H.; Sun, H.; and Liu, X. 2020 · 2020
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Precise Zero-Shot Dense Retrieval without Relevance Labels
Gao, L.; Ma, X.; Lin, J.; and Callan, J. 2022 · 2022
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Training Compute-Optimal Large Language Models
Hoffmann, J.; Borgeaud, S.; Mensch, A.; Buchatskaya, E.; Cai, T.; Rutherford, E.; Casas, D. d. L.; Hendricks, L. A.; Welbl, J.; Clark, A.; et al. 2022 · 2022
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ReACC: A Retrieval-Augmented Code Completion Framework
Lu, S.; Duan, N.; Han, H.; Guo, D.; Hwang, S.-w.; and Svyatkovskiy, A. 2022 · 2022
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Training language models to follow instructions with human feedback
Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C. L.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A.; Schulman, J.; Hilton, J.; Kelton, F.; Miller, L.; Simens, M.; Askell, A.; Welinder, P.; Christiano, P.; Leike, J.; and Lowe, R. 2022 · 2022
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BashExplainer: Retrieval-Augmented Bash Code Comment Generation based on Fine-tuned CodeBERT
Yu, C.; Yang, G.; Chen, X.; Liu, K.; and Zhou, Y. 2022 · 2022
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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. 2023 · 2023
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Fireact: Toward language agent fine-tuning
Chen, B.; Shu, C.; Shareghi, E.; Collier, N.; Narasimhan, K.; and Yao, S. 2023 · 2023
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Retrieval-Generation Synergy Augmented Large Language Models
Feng, Z.; Feng, X.; Zhao, D.; Yang, M.; and Qin, B. 2023 · 2023
Cited alongside, same era.
Huang, L.; Yu, W.; Ma, W.; Zhong, W.; Feng, Z.; Wang, H.; Chen, Q.; Peng, W.; Feng, X.; Qin, B.; and Liu, T. 2023 · 2023
Cited alongside, same era.
Query Rewriting for Retrieval-Augmented Large Language Models
Ma, X.; Gong, Y.; He, P.; Zhao, H.; and Duan, N. 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.
Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
Dubey, A.; Jauhri, A.; Pandey, A.; Kadian, A.; Al-Dahle, A.; Letman, A.; Mathur, A.; Schelten, A.; Yang, A.; Fan, A.; et al. 2024 · 2024
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Retrieval-Augmented Generation for Large Language Models: A Survey
Gao, Y.; Xiong, Y.; Gao, X.; Jia, K.; Pan, J.; Bi, Y.; Dai, Y.; Sun, J.; Guo, Q.; Wang, M.; and Wang, H. 2024 · 2024
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FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research
Jin, J.; Zhu, Y.; Yang, X.; Zhang, C.; and Dou, Z. 2024 · 2024
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Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?
Lee, J.; Chen, A.; Dai, Z.; Dua, D.; Sachan, D. S.; Boratko, M.; Luan, Y.; Arnold, S. M. R.; Perot, V.; Dalmia, S.; et al. 2024 · 2024
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Trivedi, H.; Balasubramanian, N.; Khot, T.; and Sabharwal, A. 2023 · 2023
Cited alongside, same era.
Query2doc: Query Expansion with Large Language Models
Wang, L.; Yang, N.; and Wei, F. 2023 · 2023
Cited alongside, same era.
RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair
Wang, W.; Wang, Y.; Joty, S.; and Hoi, S. C. 2023 · 2023
Cited alongside, same era.
Large Language Models as Optimizers
Yang, C.; Wang, X.; Lu, Y.; Liu, H.; Le, Q. V.; Zhou, D.; and Chen, X. 2023 · 2023
Cited alongside, same era.
ReAct: Synergizing Reasoning and Acting in Language Models
Yao, S.; Zhao, J.; Yu, D.; Du, N.; Shafran, I.; Narasimhan, K.; and Cao, Y. 2023 · 2023
Cited alongside, same era.
Zhang, S.; Fang, Q.; Zhang, Z.; Ma, Z.; Zhou, Y.; Huang, L.; Bu, M.; Gui, S.; Chen, Y.; Chen, X.; and Feng, Y. 2023 · 2023
Cited alongside, same era.
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Abdin, M.; Jacobs, S. A.; Awan, A. A.; Aneja, J.; Awadallah, A.; Awadalla, H.; Bach, N.; Bahree, A.; Bakhtiari, A.; Behl, H.; et al. 2024 · 2024
Cited alongside, same era.
RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation
Chan, C.-M.; Xu, C.; Yuan, R.; Luo, H.; Xue, W.; Guo, Y.; and Fu, J. 2024 · 2024
Cited alongside, same era.
Li, M.; Li, X.; Chen, Y.; Xuan, W.; and Zhang, W. 2024 · 2024
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ChatQA: Surpassing GPT-4 on Conversational QA and RAG
Liu, Z.; Ping, W.; Roy, R.; Xu, P.; Lee, C.; Shoeybi, M.; and Catanzaro, B. 2024 · 2024
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RaFe: Ranking Feedback Improves Query Rewriting for RAG
Mao, S.; Jiang, Y.; Chen, B.; Li, X.; Wang, P.; Wang, X.; Xie, P.; Huang, F.; Chen, H.; and Zhang, N. 2024 · 2024
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UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems
Wang, H.; Huang, W.; Deng, Y.; Wang, R.; Wang, Z.; Wang, Y.; Mi, F.; Pan, J. Z.; and Wong, K.-F. 2024 · 2024
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Wang, H.; Zhao, T.; and Gao, J. 2024 · 2024
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Retrieval meets Long Context Large Language Models
Xu, P.; Ping, W.; Wu, X.; McAfee, L.; Zhu, C.; Liu, Z.; Subramanian, S.; Bakhturina, E.; Shoeybi, M.; and Catanzaro, B. 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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Yang, A.; Yang, B.; Hui, B.; Zheng, B.; Yu, B.; Zhou, C.; Li, C.; Li, C.; Liu, D.; Huang, F.; Dong, G.; Wei, H.; Lin, H.; Tang, J.; Wang, J.; Yang, J.; Tu, J.; Zhang, J.; Ma, J.; Yang, J.; Xu, J.; Zhou, J.; Bai, J.; He, J.; Lin, J.; Dang, K.; Lu, K.; Chen, K.; Yang, K.; Li, M.; Xue, M.; Ni, N.; Zhang, P.; Wang, P.; Peng, R.; Men, R.; Gao, R.; Lin, R.; Wang, S.; Bai, S.; Tan, S.; Zhu, T.; Li, T.; Liu, T.; Ge, W.; Deng, X.; Zhou, X.; Ren, X.; Zhang, X.; Wei, X.; Ren, X.; Liu, X.; Fan, Y.; Yao, Y.; Zhang, Y.; Wan, Y.; Chu, Y.; Liu, Y.; Cui, Z.; Zhang, Z.; Guo, Z.; and Fan, Z. 2024 · 2024
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RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs
Yu, Y.; Ping, W.; Liu, Z.; Wang, B.; You, J.; Zhang, C.; Shoeybi, M.; and Catanzaro, B. 2024 · 2024
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