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2023

Retrieval-Augmented Generation for Large Language Models: A Survey

Gao, Yunfan, Xiong, Yun, Gao, Xinyu et al.

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Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes.

Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases.
This enhances the accuracy and credibility of the generation, particularly for knowledge-intensive tasks, and allows for continuous knowledge updates and integration of domain-specific information.
RAG synergistically merges LLMs' intrinsic knowledge with the vast, dynamic repositories of external databases.

Retrieval-Augmented Generation for Large Language Models: A Survey · Around

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X. Li and D. Roth, “Learning question classifiers,” in COLING 2002: The 19th International Conference on Computational Linguistics , 2002
2002
Earlier work this paper cites.
R. Steinberger, B. Pouliquen, A. Widiger, C. Ignat, T. Erjavec, D. Tufis, and D. Varga, “The jrc-acquis: A multilingual aligned parallel corpus with 20+ languages,” arXiv preprint cs/0609058 , 2006
2006
Earlier work this paper cites.
J. Berant, A. Chou, R. Frostig, and P. Liang, “Semantic parsing on freebase from question-answer pairs,” in Proceedings of the 2013 conference on empirical methods in natural language processing , 2013, pp. 1533–1544
2013
Earlier work this paper cites.
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Proceedings of the 2013 conference on empirical methods in natural language processing , 2013, pp. 1631–1642
2013
Earlier work this paper cites.
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang, “Squad: 100,000+ questions for machine comprehension of text,” arXiv preprint arXiv:1606.05250 , 2016
Original
2016
Earlier work this paper cites.
T. Nguyen, M. Rosenberg, X. Song, J. Gao, S. Tiwary, R. Majumder, and L. Deng, “Ms marco: A human-generated machine reading comprehension dataset,” 2016
2016
Earlier work this paper cites.
T.-H. Wen, M. Gasic, N. Mrksic, L. M. Rojas-Barahona, P.-H. Su, S. Ultes, D. Vandyke, and S. Young, “Conditional generation and snapshot learning in neural dialogue systems,” arXiv preprint arXiv:1606.03352 , 2016
Original
2016
Earlier work this paper cites.
R. He and J. McAuley, “Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering,” in proceedings of the 25th international conference on world wide web , 2016, pp. 507–517
2016
Earlier work this paper cites.
S. Merity, C. Xiong, J. Bradbury, and R. Socher, “Pointer sentinel mixture models,” arXiv preprint arXiv:1609.07843 , 2016
Original
2016
Earlier work this paper cites.
R. Lebret, D. Grangier, and M. Auli, “Neural text generation from structured data with application to the biography domain,” arXiv preprint arXiv:1603.07771 , 2016
Original
2016
Earlier work this paper cites.
M. Joshi, E. Choi, D. S. Weld, and L. Zettlemoyer, “Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension,” arXiv preprint arXiv:1705.03551 , 2017
Original
2017
Earlier work this paper cites.
O. Levy, M. Seo, E. Choi, and L. Zettlemoyer, “Zero-shot relation extraction via reading comprehension,” arXiv preprint arXiv:1706.04115 , 2017
Original
2017
Earlier work this paper cites.
Z. Yang, P. Qi, S. Zhang, Y. Bengio, W. W. Cohen, R. Salakhutdinov, and C. D. Manning, “Hotpotqa: A dataset for diverse, explainable multi-hop question answering,” arXiv preprint arXiv:1809.09600 , 2018
Original
2018
Earlier work this paper cites.
T. Kočiskỳ, J. Schwarz, P. Blunsom, C. Dyer, K. M. Hermann, G. Melis, and E. Grefenstette, “The narrativeqa reading comprehension challenge,” Transactions of the Association for Computational Linguistics , vol. 6, pp. 317–328, 2018
2018
Earlier work this paper cites.
P. Clark, I. Cowhey, O. Etzioni, T. Khot, A. Sabharwal, C. Schoenick, and O. Tafjord, “Think you have solved question answering? try arc, the ai2 reasoning challenge,” arXiv preprint arXiv:1803.05457 , 2018
Original
2018
Earlier work this paper cites.
A. Talmor, J. Herzig, N. Lourie, and J. Berant, “Commonsenseqa: A question answering challenge targeting commonsense knowledge,” arXiv preprint arXiv:1811.00937 , 2018
Original
2018
Earlier work this paper cites.
E. Dinan, S. Roller, K. Shuster, A. Fan, M. Auli, and J. Weston, “Wizard of wikipedia: Knowledge-powered conversational agents,” arXiv preprint arXiv:1811.01241 , 2018
Original
2018
Earlier work this paper cites.
H. Elsahar, P. Vougiouklis, A. Remaci, C. Gravier, J. Hare, F. Laforest, and E. Simperl, “T-rex: A large scale alignment of natural language with knowledge base triples,” in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) , 2018
2018
Earlier work this paper cites.
A. Saha, V. Pahuja, M. Khapra, K. Sankaranarayanan, and S. Chandar, “Complex sequential question answering: Towards learning to converse over linked question answer pairs with a knowledge graph,” in Proceedings of the AAAI conference on artificial intelligence , vol. 32, no. 1, 2018
2018
Earlier work this paper cites.
J. Thorne, A. Vlachos, C. Christodoulopoulos, and A. Mittal, “Fever: a large-scale dataset for fact extraction and verification,” arXiv preprint arXiv:1803.05355 , 2018
Original
2018
Earlier work this paper cites.
S. Narayan, S. B. Cohen, and M. Lapata, “Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization,” arXiv preprint arXiv:1808.08745 , 2018
Original
2018
Earlier work this paper cites.
T. Kwiatkowski, J. Palomaki, O. Redfield, M. Collins, A. Parikh, C. Alberti, D. Epstein, I. Polosukhin, J. Devlin, K. Lee et al. , “Natural questions: a benchmark for question answering research,” Transactions of the Association for Computational Linguistics , vol. 7, pp. 453–466, 2019
2019
Earlier work this paper cites.
A. Fan, Y. Jernite, E. Perez, D. Grangier, J. Weston, and M. Auli, “Eli5: Long form question answering,” arXiv preprint arXiv:1907.09190 , 2019
Original
2019
Earlier work this paper cites.
S. Ebner, P. Xia, R. Culkin, K. Rawlins, and B. Van Durme, “Multi-sentence argument linking,” arXiv preprint arXiv:1911.03766 , 2019
Original
2019
Earlier work this paper cites.
R. Zellers, A. Holtzman, Y. Bisk, A. Farhadi, and Y. Choi, “Hellaswag: Can a machine really finish your sentence?” arXiv preprint arXiv:1905.07830 , 2019
Original
2019
Earlier work this paper cites.
H. Husain, H.-H. Wu, T. Gazit, M. Allamanis, and M. Brockschmidt, “Codesearchnet challenge: Evaluating the state of semantic code search,” arXiv preprint arXiv:1909.09436 , 2019
Original
2019
Earlier work this paper cites.
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel et al. , “Retrieval-augmented generation for knowledge-intensive nlp tasks,” Advances in Neural Information Processing Systems , vol. 33, pp. 9459–9474, 2020
2020
Earlier work this paper cites.
V. Karpukhin, B. Oğuz, S. Min, P. Lewis, L. Wu, S. Edunov, D. Chen, and W.-t. Yih, “Dense passage retrieval for open-domain question answering,” arXiv preprint arXiv:2004.04906 , 2020
Original
2020
Earlier work this paper cites.
X. Ho, A.-K. D. Nguyen, S. Sugawara, and A. Aizawa, “Constructing a multi-hop qa dataset for comprehensive evaluation of reasoning steps,” arXiv preprint arXiv:2011.01060 , 2020
Original
2020
Earlier work this paper cites.
T. Möller, A. Reina, R. Jayakumar, and M. Pietsch, “Covid-qa: A question answering dataset for covid-19,” in ACL 2020 Workshop on Natural Language Processing for COVID-19 (NLP-COVID) , 2020
2020
Earlier work this paper cites.
D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt, “Measuring massive multitask language understanding,” arXiv preprint arXiv:2009.03300 , 2020
Original
2020
Earlier work this paper cites.
N. Kotonya and F. Toni, “Explainable automated fact-checking for public health claims,” arXiv preprint arXiv:2010.09926 , 2020
Original
2020
Earlier work this paper cites.
J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei, “Scaling laws for neural language models,” arXiv preprint arXiv:2001.08361 , 2020
Original
2020
Earlier work this paper cites.
R. Nakano, J. Hilton, S. Balaji, J. Wu, L. Ouyang, C. Kim, C. Hesse, S. Jain, V. Kosaraju, W. Saunders et al. , “Webgpt: Browser-assisted question-answering with human feedback,” arXiv preprint arXiv:2112.09332 , 2021
Original
2021
Earlier work this paper cites.
M. Zhong, D. Yin, T. Yu, A. Zaidi, M. Mutuma, R. Jha, A. H. Awadallah, A. Celikyilmaz, Y. Liu, X. Qiu et al. , “Qmsum: A new benchmark for query-based multi-domain meeting summarization,” arXiv preprint arXiv:2104.05938 , 2021
Original
2021
Earlier work this paper cites.
P. Dasigi, K. Lo, I. Beltagy, A. Cohan, N. A. Smith, and M. Gardner, “A dataset of information-seeking questions and answers anchored in research papers,” arXiv preprint arXiv:2105.03011 , 2021
Original
2021
Earlier work this paper cites.
R. Y. Pang, A. Parrish, N. Joshi, N. Nangia, J. Phang, A. Chen, V. Padmakumar, J. Ma, J. Thompson, H. He et al. , “Quality: Question answering with long input texts, yes!” arXiv preprint arXiv:2112.08608 , 2021
Original
2021
Earlier work this paper cites.
S. Li, H. Ji, and J. Han, “Document-level event argument extraction by conditional generation,” arXiv preprint arXiv:2104.05919 , 2021
Original
2021
Earlier work this paper cites.
M. Geva, D. Khashabi, E. Segal, T. Khot, D. Roth, and J. Berant, “Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies,” Transactions of the Association for Computational Linguistics , vol. 9, pp. 346–361, 2021
2021
Earlier work this paper cites.
H. Hayashi, P. Budania, P. Wang, C. Ackerson, R. Neervannan, and G. Neubig, “Wikiasp: A dataset for multi-domain aspect-based summarization,” Transactions of the Association for Computational Linguistics , vol. 9, pp. 211–225, 2021
2021
Earlier work this paper cites.
K. Cobbe, V. Kosaraju, M. Bavarian, M. Chen, H. Jun, L. Kaiser, M. Plappert, J. Tworek, J. Hilton, R. Nakano et al. , “Training verifiers to solve math word problems,” arXiv preprint arXiv:2110.14168 , 2021
Original
2021
Earlier work this paper cites.
S. Borgeaud, A. Mensch, J. Hoffmann, T. Cai, E. Rutherford, K. Millican, G. B. Van Den Driessche, J.-B. Lespiau, B. Damoc, A. Clark et al. , “Improving language models by retrieving from trillions of tokens,” in International conference on machine learning . PMLR, 2022, pp. 2206–2240
2022
Earlier work this paper cites.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Advances in neural information processing systems , vol. 35, pp. 27 730–27 744, 2022
2022
Earlier work this paper cites.
L. Gao, X. Ma, J. Lin, and J. Callan, “Precise zero-shot dense retrieval without relevance labels,” arXiv preprint arXiv:2212.10496 , 2022
Original
2022
Earlier work this paper cites.
W. Yu, D. Iter, S. Wang, Y. Xu, M. Ju, S. Sanyal, C. Zhu, M. Zeng, and M. Jiang, “Generate rather than retrieve: Large language models are strong context generators,” arXiv preprint arXiv:2209.10063 , 2022
Original
2022
Earlier work this paper cites.
S. Wang, Y. Xu, Y. Fang, Y. Liu, S. Sun, R. Xu, C. Zhu, and M. Zeng, “Training data is more valuable than you think: A simple and effective method by retrieving from training data,” arXiv preprint arXiv:2203.08773 , 2022
Original
2022
Earlier work this paper cites.
Z. Dai, V. Y. Zhao, J. Ma, Y. Luan, J. Ni, J. Lu, A. Bakalov, K. Guu, K. B. Hall, and M.-W. Chang, “Promptagator: Few-shot dense retrieval from 8 examples,” arXiv preprint arXiv:2209.11755 , 2022
Original
2022
Earlier work this paper cites.
Z. Sun, X. Wang, Y. Tay, Y. Yang, and D. Zhou, “Recitation-augmented language models,” arXiv preprint arXiv:2210.01296 , 2022
Original
2022
Earlier work this paper cites.
O. Khattab, K. Santhanam, X. L. Li, D. Hall, P. Liang, C. Potts, and M. Zaharia, “Demonstrate-search-predict: Composing retrieval and language models for knowledge-intensive nlp,” arXiv preprint arXiv:2212.14024 , 2022
Original
2022
Earlier work this paper cites.
T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, “Copy is all you need,” in The Eleventh International Conference on Learning Representations , 2022
2022
Earlier work this paper cites.
X. Du and H. Ji, “Retrieval-augmented generative question answering for event argument extraction,” arXiv preprint arXiv:2211.07067 , 2022
Original
2022
Earlier work this paper cites.
G. Izacard, P. Lewis, M. Lomeli, L. Hosseini, F. Petroni, T. Schick, J. Dwivedi-Yu, A. Joulin, S. Riedel, and E. Grave, “Few-shot learning with retrieval augmented language models,” arXiv preprint arXiv:2208.03299 , 2022
Original
2022
Earlier work this paper cites.
H. Trivedi, N. Balasubramanian, T. Khot, and A. Sabharwal, “Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions,” arXiv preprint arXiv:2212.10509 , 2022
Original
2022
Earlier work this paper cites.
Z. Wang, X. Pan, D. Yu, D. Yu, J. Chen, and H. Ji, “Zemi: Learning zero-shot semi-parametric language models from multiple tasks,” arXiv preprint arXiv:2210.00185 , 2022
Original
2022
Earlier work this paper cites.
M. Gaur, K. Gunaratna, V. Srinivasan, and H. Jin, “Iseeq: Information seeking question generation using dynamic meta-information retrieval and knowledge graphs,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 10, 2022, pp. 10 672–10 680
2022
Earlier work this paper cites.
D. Zhou, N. Schärli, L. Hou, J. Wei, N. Scales, X. Wang, D. Schuurmans, C. Cui, O. Bousquet, Q. Le et al. , “Least-to-most prompting enables complex reasoning in large language models,” arXiv preprint arXiv:2205.10625 , 2022
Original
2022
Earlier work this paper cites.
N. Anderson, C. Wilson, and S. D. Richardson, “Lingua: Addressing scenarios for live interpretation and automatic dubbing,” in Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track) , J. Campbell, S. Larocca, J. Marciano, K. Savenkov, and A. Yanishevsky, Eds. Orlando, USA: Association for Machine Translation in the Americas, Sep. 2022, pp. 202–209. [Online]. Available: https://aclanthology.org/2022.amta-upg.14
2022
Earlier work this paper cites.
A. Mallen, A. Asai, V. Zhong, R. Das, H. Hajishirzi, and D. Khashabi, “When not to trust language models: Investigating effectiveness and limitations of parametric and non-parametric memories,” arXiv preprint arXiv:2212.10511 , 2022
Original
2022
Earlier work this paper cites.
H. Trivedi, N. Balasubramanian, T. Khot, and A. Sabharwal, “Musique: Multihop questions via single-hop question composition,” Transactions of the Association for Computational Linguistics , vol. 10, pp. 539–554, 2022
2022
Earlier work this paper cites.
I. Stelmakh, Y. Luan, B. Dhingra, and M.-W. Chang, “Asqa: Factoid questions meet long-form answers,” arXiv preprint arXiv:2204.06092 , 2022
Original
2022
Earlier work this paper cites.
X. Xu, Z. Gou, W. Wu, Z.-Y. Niu, H. Wu, H. Wang, and S. Wang, “Long time no see! open-domain conversation with long-term persona memory,” arXiv preprint arXiv:2203.05797 , 2022
Original
2022
Earlier work this paper cites.

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Cited alongside, same era.

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Cited alongside, same era.

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Cited alongside, same era.

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Cited alongside, same era.

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Cited alongside, same era.

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Cited alongside, same era.

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Cited alongside, same era.

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Cited alongside, same era.

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Original

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H. Wang, M. Hu, Y. Deng, R. Wang, F. Mi, W. Wang, Y. Wang, W.-C. Kwan, I. King, and K.-F. Wong, “Large language models as source planner for personalized knowledge-grounded dialogue,” arXiv preprint arXiv:2310.08840 , 2023

2023

Closest in time.

——, “Large language models as source planner for personalized knowledge-grounded dialogue,” arXiv preprint arXiv:2310.08840 , 2023

2023

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S. Kim, S. J. Joo, D. Kim, J. Jang, S. Ye, J. Shin, and M. Seo, “The cot collection: Improving zero-shot and few-shot learning of language models via chain-of-thought fine-tuning,” arXiv preprint arXiv:2305.14045 , 2023

2023

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S. Saha, J. A. Junaed, M. Saleki, A. S. Sharma, M. R. Rifat, M. Rahouti, S. I. Ahmed, N. Mohammed, and M. R. Amin, “Vio-lens: A novel dataset of annotated social network posts leading to different forms of communal violence and its evaluation,” in Proceedings of the First Workshop on Bangla Language Processing (BLP-2023) , 2023, pp. 72–84

2023

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Y. Hoshi, D. Miyashita, Y. Ng, K. Tatsuno, Y. Morioka, O. Torii, and J. Deguchi, “Ralle: A framework for developing and evaluating retrieval-augmented large language models,” arXiv preprint arXiv:2308.10633 , 2023

2023

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J. Liu, “Building production-ready rag applications,” https://www.ai.engineer/summit/schedule/building-production-ready-rag-applications , 2023

2023

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I. Nguyen, “Evaluating rag part i: How to evaluate document retrieval,” https://www.deepset.ai/blog/rag-evaluation-retrieval , 2023

2023

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Q. Leng, K. Uhlenhuth, and A. Polyzotis, “Best practices for llm evaluation of rag applications,” https://www.databricks.com/blog/LLM-auto-eval-best-practices-RAG , 2023

2023

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S. Es, J. James, L. Espinosa-Anke, and S. Schockaert, “Ragas: Automated evaluation of retrieval augmented generation,” arXiv preprint arXiv:2309.15217 , 2023

Original

2023

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J. Saad-Falcon, O. Khattab, C. Potts, and M. Zaharia, “Ares: An automated evaluation framework for retrieval-augmented generation systems,” arXiv preprint arXiv:2311.09476 , 2023

2023

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C. Jarvis and J. Allard, “A survey of techniques for maximizing llm performance,” https://community.openai.com/t/openai-dev-day-2023-breakout-sessions/505213#a-survey-of-techniques-for-maximizing-llm-performance-2 , 2023

2023

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J. Chen, H. Lin, X. Han, and L. Sun, “Benchmarking large language models in retrieval-augmented generation,” arXiv preprint arXiv:2309.01431 , 2023

2023

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Y. Liu, L. Huang, S. Li, S. Chen, H. Zhou, F. Meng, J. Zhou, and X. Sun, “Recall: A benchmark for llms robustness against external counterfactual knowledge,” arXiv preprint arXiv:2311.08147 , 2023

2023

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P. Xu, W. Ping, X. Wu, L. McAfee, C. Zhu, Z. Liu, S. Subramanian, E. Bakhturina, M. Shoeybi, and B. Catanzaro, “Retrieval meets long context large language models,” arXiv preprint arXiv:2310.03025 , 2023

2023

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C. Packer, V. Fang, S. G. Patil, K. Lin, S. Wooders, and J. E. Gonzalez, “Memgpt: Towards llms as operating systems,” arXiv preprint arXiv:2310.08560 , 2023

2023

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G. Xiao, Y. Tian, B. Chen, S. Han, and M. Lewis, “Efficient streaming language models with attention sinks,” arXiv preprint arXiv:2309.17453 , 2023

2023

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J. Li, D. Li, S. Savarese, and S. Hoi, “Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,” arXiv preprint arXiv:2301.12597 , 2023

2023

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D. M. Chan, S. Ghosh, A. Rastrow, and B. Hoffmeister, “Using external off-policy speech-to-text mappings in contextual end-to-end automated speech recognition,” arXiv preprint arXiv:2301.02736 , 2023

2023

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A. Yang, A. Nagrani, P. H. Seo, A. Miech, J. Pont-Tuset, I. Laptev, J. Sivic, and C. Schmid, “Vid2seq: Large-scale pretraining of a visual language model for dense video captioning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 10 714–10 726

2023

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N. Nashid, M. Sintaha, and A. Mesbah, “Retrieval-based prompt selection for code-related few-shot learning,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) , 2023, pp. 2450–2462

2023

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Z. Ke, W. Kong, C. Li, M. Zhang, Q. Mei, and M. Bendersky, “Bridging the preference gap between retrievers and llms,” arXiv preprint arXiv:2401.06954 , 2024

2024

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M. Seo, J. Baek, J. Thorne, and S. J. Hwang, “Retrieval-augmented data augmentation for low-resource domain tasks,” arXiv preprint arXiv:2402.13482 , 2024

2024

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F. Cuconasu, G. Trappolini, F. Siciliano, S. Filice, C. Campagnano, Y. Maarek, N. Tonellotto, and F. Silvestri, “The power of noise: Redefining retrieval for rag systems,” arXiv preprint arXiv:2401.14887 , 2024

2024

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P. Sarthi, S. Abdullah, A. Tuli, S. Khanna, A. Goldie, and C. D. Manning, “Raptor: Recursive abstractive processing for tree-organized retrieval,” arXiv preprint arXiv:2401.18059 , 2024

2024

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S.-Q. Yan, J.-C. Gu, Y. Zhu, and Z.-H. Ling, “Corrective retrieval augmented generation,” arXiv preprint arXiv:2401.15884 , 2024

2024

Closest in time.

H. Wang, W. Huang, Y. Deng, R. Wang, Z. Wang, Y. Wang, F. Mi, J. Z. Pan, and K.-F. Wong, “Unims-rag: A unified multi-source retrieval-augmented generation for personalized dialogue systems,” arXiv preprint arXiv:2401.13256 , 2024

2024

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X. He, Y. Tian, Y. Sun, N. V. Chawla, T. Laurent, Y. LeCun, X. Bresson, and B. Hooi, “G-retriever: Retrieval-augmented generation for textual graph understanding and question answering,” arXiv preprint arXiv:2402.07630 , 2024

2024

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K.-H. Lee, X. Chen, H. Furuta, J. Canny, and I. Fischer, “A human-inspired reading agent with gist memory of very long contexts,” arXiv preprint arXiv:2402.09727 , 2024

2024

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Y. Lyu, Z. Li, S. Niu, F. Xiong, B. Tang, W. Wang, H. Wu, H. Liu, T. Xu, and E. Chen, “Crud-rag: A comprehensive chinese benchmark for retrieval-augmented generation of large language models,” arXiv preprint arXiv:2401.17043 , 2024

2024

Closest in time.

T. Zhang, S. G. Patil, N. Jain, S. Shen, M. Zaharia, I. Stoica, and J. E. Gonzalez, “Raft: Adapting language model to domain specific rag,” arXiv preprint arXiv:2403.10131 , 2024

2024

Closest in time.