📄 Retrieval-Augmented Generation for AI-Generated Content: A Survey

Paper · 2024 · Penghao Zhao, Hailin Zhang, Qinhan Yu et al.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

🏛️ Institutions

🎓Peking UniversityBeijing · China244 works · 39,359 citations🏢TencentShenzhen · China134 works · 19,743 citations🎓Shanghai Jiao Tong UniversityShanghai · China149 works · 16,161 citations

🧭 Fields, topics and concepts

Cite

Cite as Penghao Zhao, Hailin Zhang, Qinhan Yu, Zhengren Wang, Yunteng Geng, Fangcheng Fu, Ling Yang, Wentao Zhang, Jie Jiang and Bin Cui. 2024. Retrieval-Augmented Generation for AI-Generated Content: A Survey. arXiv:2402.19473.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2402.19473 · arXiv 2402.19473

@misc{zhao2024retrieval,
  title = {{Retrieval-Augmented Generation for AI-Generated Content: A Survey}},
  author = {Penghao Zhao, Hailin Zhang, Qinhan Yu, Zhengren Wang, Yunteng Geng, Fangcheng Fu, Ling Yang, Wentao Zhang, Jie Jiang and Bin Cui},
  year = {2024},
  doi = {10.48550/arxiv.2402.19473},
  eprint = {2402.19473},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2402.19473}
}

References

353 references, 84 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. T. B. Brown, B. Mann et al., “Language models are few-shot learners,” in NeurIPS, 2020.
2. M. Chen, J. Tworek et al., “Evaluating large language models trained on code,” arXiv:2107.03374, 2021.
3. OpenAI, “GPT-4 technical report,” arXiv:2303.08774, 2023.
4. H. Touvron, T. Lavril et al., “Llama: Open and efficient foundation language models,” arXiv:2302.13971, 2023.
5. H. Touvron, L. Martin et al., “Llama 2: Open foundation and fine-tuned chat models,” arXiv:2307.09288, 2023.
6. B. Rozière, J. Gehring et al., “Code llama: Open foundation models for code,” arXiv:2308.12950, 2023.
7. A. Ramesh, M. Pavlov, G. Goh et al., “Zero-shot text-to-image generation,” in ICML, 2021.
8. A. Ramesh, P. Dhariwal, A. Nichol et al., “Hierarchical text-conditional image generation with CLIP latents,” arXiv:2204.06125, 2022.
9. J. Betker, G. Goh, L. Jing et al., “Improving image generation with better captions,” Computer Science, vol. 2, no. 3, p. 8, 2023. find
10. R. Rombach, A. Blattmann, D. Lorenz et al., “High-resolution image synthesis with latent diffusion models,” in IEEE/CVF, 2022. find
11. OpenAI, “Video generation models as world simulators,” https://openai.com/research/video-generation-models-as-world-simulators, 2024. ↗
12. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997. find
13. A. Vaswani, N. Shazeer, N. Parmar et al., “Attention is all you need,” in NeurIPS, 2017.
14. I. Goodfellow, J. Pouget-Abadie, M. Mirza et al., “Generative adversarial networks,” CACM, vol. 63, no. 11, pp. 139–144, 2020. find
15. J. Devlin, M. Chang et al., “BERT: pre-training of deep bidirectional transformers for language understanding,” in NAACL-HLT, 2019.
16. C. Raffel, N. Shazeer, A. Roberts et al., “Exploring the limits of transfer learning with a unified text-to-text transformer,” JMLR, vol. 21, pp. 140:1–140:67, 2020.
17. W. Fedus, B. Zoph, and N. Shazeer, “Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,” JMLR, vol. 23, no. 120, pp. 1–39, 2022. find
18. J. Kaplan, S. McCandlish, T. Henighan et al., “Scaling laws for neural language models,” 2020.
19. S. E. Robertson and H. Zaragoza, “The probabilistic relevance framework: BM25 and beyond,” FTIR, vol. 3, no. 4, pp. 333–389, 2009. find
20. V. Karpukhin, B. Oguz, S. Min et al., “Dense passage retrieval for open-domain question answering,” in EMNLP, 2020.
21. J. Johnson, M. Douze, and H. Jégou, “Billion-scale similarity search with gpus,” IEEE Trans. Big Data, vol. 7, no. 3, pp. 535–547, 2021. find
22. Q. Chen, B. Zhao, H. Wang et al., “SPANN: highly-efficient billion-scale approximate nearest neighborhood search,” in NeurIPS, 2021. find
23. R. Datta, D. Joshi, J. Li et al., “Image retrieval: Ideas, influences, and trends of the new age,” CSUR, vol. 40, no. 2, pp. 5:1–5:60, 2008. find
24. A. Radford, J. W. Kim, C. Hallacy et al., “Learning transferable visual models from natural language supervision,” in ICML, 2021.
25. Z. Feng, D. Guo et al., “Codebert: A pre-trained model for programming and natural languages,” in EMNLP Findings, 2020.
26. Y. Wu, K. Chen, T. Zhang et al., “Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation,” in ICASSP, 2023. find
27. A. Mallen, A. Asai, V. Zhong et al., “When not to trust language models: Investigating effectiveness of parametric and non-parametric memories,” in ACL, 2023. find
28. N. Carlini, F. Tramèr et al., “Extracting training data from large language models,” in USENIX, 2021.
29. M. Kang, N. M. Gürel et al., “C-RAG: certified generation risks for retrieval-augmented language models,” arXiv:2402.03181, 2024. find
30. G. Izacard, P. Lewis, M. Lomeli et al., “Atlas: Few-shot learning with retrieval augmented language models,” arXiv:2208.03299, 2022.
31. Y. Wu, M. N. Rabe, D. Hutchins, and C. Szegedy, “Memorizing transformers,” in ICLR, 2022. find
32. Z. He, Z. Zhong, T. Cai et al., “REST: retrieval-based speculative decoding,” arxiv:2311.08252, 2023. find
33. K. Guu, K. Lee, Z. Tung et al., “REALM: retrieval-augmented language model pre-training,” ICML, 2020.
34. P. S. H. Lewis, E. Perez, A. Piktus et al., “Retrieval-augmented generation for knowledge-intensive NLP tasks,” in NeurIPS, 2020.
35. G. Izacard and E. Grave, “Leveraging passage retrieval with generative models for open domain question answering,” in EACL, 2021.
36. S. Borgeaud, A. Mensch et al., “Improving language models by retrieving from trillions of tokens,” in ICML, 2022. find
37. U. Khandelwal, O. Levy, D. Jurafsky et al., “Generalization through memorization: Nearest neighbor language models,” in ICLR, 2020. find
38. J. He, G. Neubig, and T. Berg-Kirkpatrick, “Efficient nearest neighbor language models,” in EMNLP, 2021.
39. zilliztech. (2023) Gptcache. [Online]. Available: https://github.com/zilliztech/GPTCache ↗
40. M. R. Parvez, W. U. Ahmad et al., “Retrieval augmented code generation and summarization,” in EMNLP Findings, 2021.
41. W. U. Ahmad, S. Chakraborty, B. Ray et al., “Unified pre-training for program understanding and generation,” in NAACL-HLT, 2021.
42. S. Zhou, U. Alon, F. F. Xu et al., “Docprompting: Generating code by retrieving the docs,” in ICLR, 2023. find
43. Y. Koizumi, Y. Ohishi et al., “Audio captioning using pre-trained large-scale language model guided by audio-based similar caption retrieval,” arXiv:2012.07331, 2020. — [arXiv:2012.07331](https://arxiv.org/abs/2012.07331) find
44. R. Huang, J. Huang, D. Yang et al., “Make-an-audio: Text-to-audio generation with prompt-enhanced diffusion models,” in ICML, 2023. find
45. H.-Y. Tseng, H.-Y. Lee et al., “Retrievegan: Image synthesis via differentiable patch retrieval,” in ECCV, 2020. find
46. S. Sarto, M. Cornia, L. Baraldi, and R. Cucchiara, “Retrieval-augmented transformer for image captioning,” in CBMI, 2022. find
47. R. Ramos, B. Martins et al., “Smallcap: lightweight image captioning prompted with retrieval augmentation,” in CVPR, 2023. find
48. J. Chen, Y. Pan, Y. Li et al., “Retrieval augmented convolutional encoder-decoder networks for video captioning,” TOMCCAP, vol. 19, no. 1s, pp. 48:1–48:24, 2023. find
49. J. Xu, Y. Huang, J. Hou et al., “Retrieval-augmented egocentric video captioning,” arXiv:2401.00789, 2024. — [arXiv:2401.00789](https://arxiv.org/abs/2401.00789) find
50. J. Seo, S. Hong et al., “Retrieval-augmented score distillation for text-to-3d generation,” arXiv:2402.02972, 2024. — [arXiv:2402.02972](https://arxiv.org/abs/2402.02972) find
51. M. Zhang, X. Guo, L. Pan et al., “Remodiffuse: Retrieval-augmented motion diffusion model,” in ICCV, 2023. find
52. X. Hu, X. Wu, Y. Shu, and Y. Qu, “Logical form generation via multi-task learning for complex question answering over knowledge bases,” in COLING, 2022. find
53. X. Huang, J. Kim, and B. Zou, “Unseen entity handling in complex question answering over knowledge base via language generation,” in EMNLP Findings, 2021. find
54. R. Das, M. Zaheer, D. Thai et al., “Case-based reasoning for natural language queries over knowledge bases,” in EMNLP, 2021. find
55. Z. Wang, W. Nie, Z. Qiao et al., “Retrieval-based controllable molecule generation,” in ICLR, 2022. find
56. Q. Jin, Y. Yang, Q. Chen, and Z. Lu, “Genegpt: Augmenting large language models with domain tools for improved access to biomedical information,” Bioinformatics, vol. 40, no. 2, p. btae075, 2024.
57. H. Li, Y. Su, D. Cai et al., “A survey on retrieval-augmented text generation,” arxiv:2202.01110, 2022. — [arXiv:2202.01110](https://arxiv.org/abs/2202.01110) find
58. A. Asai, S. Min, Z. Zhong, and D. Chen, “Acl 2023 tutorial: Retrieval-based language models and applications,” ACL 2023, 2023.
59. Y. Gao, Y. Xiong et al., “Retrieval-augmented generation for large language models: A survey,” arxiv:2312.10997, 2023.
60. R. Zhao, H. Chen et al., “Retrieving multimodal information for augmented generation: A survey,” in EMNLP, 2023. find
61. Y. Ding, W. Fan et al., “A survey on rag meets llms: Towards retrieval-augmented large language models,” arXiv:2405.06211, 2024.
62. J. Chen, H. Guo, K. Yi et al., “Visualgpt: Data-efficient adaptation of pretrained language models for image captioning,” in CVPR, 2022. find
63. Y. Tay, M. Dehghani, D. Bahri, and D. Metzler, “Efficient transformers: A survey,” CSUR, vol. 55, no. 6, pp. 109:1–109:28, 2023. find
64. G. V. Houdt et al., “A review on the long short-term memory model,” Artif. Intell. Rev., vol. 53, no. 8, pp. 5929–5955, 2020. find
65. L. Yang, Z. Zhang et al., “Diffusion models: A comprehensive survey of methods and applications,” CSUR, vol. 56, no. 4, pp. 1–39, 2023. find
66. J. Gui, Z. Sun, Y. Wen et al., “A review on generative adversarial networks: Algorithms, theory, and applications,” TKDE, vol. 35, no. 4, pp. 3313–3332, 2023. find
67. S. E. Robertson and S. Walker, “On relevance weights with little relevance information,” in SIGIR, 1997. find
68. J. D. Lafferty and C. Zhai, “Document language models, query models, and risk minimization for information retrieval,” in SIGIR, 2001. find
69. S. Hershey, S. Chaudhuri et al., “CNN architectures for large-scale audio classification,” in ICASSP, 2017. find
70. J. Dong, X. Li, C. Xu et al., “Dual encoding for zero-example video retrieval,” in CVPR, 2019. find
71. L. Xiong, C. Xiong, Y. Li et al., “Approximate nearest neighbor negative contrastive learning for dense text retrieval,” in ICLR, 2021.
72. J. L. Bentley, “Multidimensional binary search trees used for associative searching,” CACM, vol. 18, no. 9, pp. 509–517, 1975. find
73. W. Li, C. Feng, D. Lian et al., “Learning balanced tree indexes for large-scale vector retrieval,” in SIGKDDg, 2023. find
74. M. Datar, N. Immorlica, P. Indyk et al., “Locality-sensitive hashing scheme based on p-stable distributions,” in SCG, 2004. find
75. Y. A. Malkov and D. A. Yashunin, “Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs,” TPAMI, vol. 42, no. 4, pp. 824–836, 2018.
76. S. Jayaram Subramanya, F. Devvrit et al., “Diskann: Fast accurate billion-point nearest neighbor search on a single node,” NeurIPS, 2019. find
77. Y. Wang, Y. Hou, H. Wang et al., “A neural corpus indexer for document retrieval,” in NeurIPS, 2022. find
78. H. Zhang, Y. Wang, Q. Chen et al., “Model-enhanced vector index,” in NeurIPS, 2023. find
79. S. A. Hayati, R. Olivier, P. Avvaru et al., “Retrieval-based neural code generation,” in EMNLP, 2018. find
80. J. Zhang, X. Wang, H. Zhang et al., “Retrieval-based neural source code summarization,” in ICSE, 2020. find
81. G. Poesia, A. Polozov, V. Le et al., “Synchromesh: Reliable code generation from pre-trained language models,” in ICLR, 2022. find
82. X. Ye, S. Yavuz et al., “RNG-KBQA: generation augmented iterative ranking for knowledge base question answering,” in ACL, 2022. find
83. Y. Shu et al., “TIARA: multi-grained retrieval for robust question answering over large knowledge bases,” arXiv:2210.12925, 2022. — [arXiv:2210.12925](https://arxiv.org/abs/2210.12925) find
84. X. V. Lin, R. Socher et al., “Bridging textual and tabular data for cross-domain text-to-sql semantic parsing,” arXiv:2012.12627, 2020.
85. A. Asai, Z. Wu, Y. Wang et al., “Self-rag: Learning to retrieve, generate, and critique through self-reflection,” arxiv:2310.11511, 2023.
86. W. Shi, S. Min, M. Yasunaga et al., “Replug: Retrieval-augmented black-box language models,” arXiv:2301.12652, 2023.
87. O. Ram, Y. Levine, I. Dalmedigos et al., “In-context retrieval-augmented language models,” arXiv:2302.00083, 2023.
88. D. Zan, B. Chen, Z. Lin et al., “When language model meets private library,” in EMNLP Findings, 2022. find
89. N. Nashid, M. Sintaha, and A. Mesbah, “Retrieval-based prompt selection for code-related few-shot learning,” in ICSE, 2023. find
90. M. Jin, S. Shahriar, M. Tufano et al., “Inferfix: End-to-end program repair with llms,” in ESEC/FSE, 2023. find
91. S. Lu, N. Duan, H. Han et al., “Reacc: A retrieval-augmented code completion framework,” in ACL, 2022.
92. Y. Liu et al., “Uni-parser: Unified semantic parser for question answering on knowledge base and database,” in EMNLP, 2022. find
93. Z. Yang, X. Du, E. Cambria et al., “End-to-end case-based reasoning for commonsense knowledge base completion,” in EACL, 2023. find
94. W. Shi, Y. Zhuang, Y. Zhu et al., “Retrieval-augmented large language models for adolescent idiopathic scoliosis patients in shared decision-making,” in ACM-BCB, 2023. find
95. A. Casanova, M. Careil, J. Verbeek et al., “Instance-conditioned gan,” in NeurIPS, 2021. find
96. A. Bertsch, U. Alon, G. Neubig, and M. R. Gormley, “Unlimiformer: Long-range transformers with unlimited length input,” 2023. find
97. Y. Kuratov, A. Bulatov et al., “In search of needles in a 10m haystack: Recurrent memory finds what llms miss,” arXiv:2402.10790, 2024. — [arXiv:2402.10790](https://arxiv.org/abs/2402.10790) find
98. J. Li, Y. Li, G. Li et al., “Editsum: A retrieve-and-edit framework for source code summarization,” in ASE, 2021. find
99. C. Yu, G. Yang, X. Chen et al., “Bashexplainer: Retrieval-augmented bash code comment generation based on fine-tuned codebert,” in ICSME, 2022. find
100. T. B. Hashimoto, K. Guu, Y. Oren, and P. Liang, “A retrieve-and-edit framework for predicting structured outputs,” in NeurIPS, 2018. find
101. B. Wei, Y. Li, G. Li et al., “Retrieve and refine: Exemplar-based neural comment generation,” in ASE, 2020. find
102. E. Shi, Y. Wang, W. Tao et al., “RACE: retrieval-augmented commit message generation,” in EMNLP, 2022. find
103. W. Chen, H. Hu, C. Saharia, and W. W. Cohen, “Re-imagen: Retrieval-augmented text-to-image generator,” in ICLR, 2023. find
104. S. Sheynin, O. Ashual, A. Polyak et al., “Knn-diffusion: Image generation via large-scale retrieval,” in ICLR, 2023. find
105. A. Blattmann, R. Rombach, K. Oktay et al., “Retrieval-augmented diffusion models,” in NeurIPS, 2022. find
106. R. Rombach, A. Blattmann, and B. Ommer, “Text-guided synthesis of artistic images with retrieval-augmented diffusion models,” arXiv:2207.13038, 2022. — [arXiv:2207.13038](https://arxiv.org/abs/2207.13038) find
107. B. Li, P. H. Torr, and T. Lukasiewicz, “Memory-driven text-to-image generation,” arXiv:2208.07022, 2022. — [arXiv:2208.07022](https://arxiv.org/abs/2208.07022) find
108. B. Oguz, X. Chen, V. Karpukhin et al., “Unik-qa: Unified representations of structured and unstructured knowledge for open-domain question answering,” in NAACL Findings, 2022.
109. D. Yu, S. Zhang et al., “Decaf: Joint decoding of answers and logical forms for question answering over knowledge bases,” in ICLR, 2023. find
110. G. Dong, R. Li, S. Wang et al., “Bridging the kb-text gap: Leveraging structured knowledge-aware pre-training for KBQA,” in CIKM, 2023. find
111. K. Wang, F. Duan, S. Wang et al., “Knowledge-driven cot: Exploring faithful reasoning in llms for knowledge-intensive question answering,” arXiv:2308.13259, 2023. — [arXiv:2308.13259](https://arxiv.org/abs/2308.13259) find
112. D. Yu and Y. Yang, “Retrieval-enhanced generative model for large-scale knowledge graph completion,” in SIGIR, 2023. find
113. T. Févry, L. B. Soares et al., “Entities as experts: Sparse memory access with entity supervision,” in EMNLP, 2020.
114. M. de Jong, Y. Zemlyanskiy, N. FitzGerald et al., “Mention memory: incorporating textual knowledge into transformers through entity mention attention,” in ICLR, 2021. find
115. B. Jing, Y. Zhang, Z. Song et al., “Amd: Anatomical motion diffusion with interpretable motion decomposition and fusion,” in AAAI, 2024. find
116. Y. Yuan, H. Liu, X. Liu et al., “Retrieval-augmented text-to-audio generation,” in ICASSP, 2024. find
117. B. Yang, M. Cao, and Y. Zou, “Concept-aware video captioning: Describing videos with effective prior information,” TIP, vol. 32, pp. 5366–5378, 2023. find
118. Z. Zhong, T. Lei, and D. Chen, “Training language models with memory augmentation,” in EMNLP, 2022. find
119. S. Min, W. Shi, M. Lewis et al., “Nonparametric masked language modeling,” in ACL Findings, 2023. find
120. X. Zhang, Y. Zhou, G. Yang, and T. Chen, “Syntax-aware retrieval augmented code generation,” in EMNLP Findings, 2023. find
121. Z. Fei, “Memory-augmented image captioning,” in AAAI, 2021. find
122. Y. Leviathan, M. Kalman, and Y. Matias, “Fast inference from transformers via speculative decoding,” in ICML, 2023. find
123. T. Lan, D. Cai, Y. Wang et al., “Copy is all you need,” in ICLR, 2023. find
124. B. Cao, D. Cai, L. Cui et al., “Retrieval is accurate generation,” arXiv:2402.17532, 2024. — [arXiv:2402.17532](https://arxiv.org/abs/2402.17532) find
125. L. Wang, N. Yang, and F. Wei, “Query2doc: Query expansion with large language models,” in EMNLP, 2023.
126. L. Gao, X. Ma, J. Lin, and J. Callan, “Precise zero-shot dense retrieval without relevance labels,” in ACL, 2023.
127. G. Kim, S. Kim, B. Jeon et al., “Tree of clarifications: Answering ambiguous questions with retrieval-augmented large language models,” in EMNLP, 2023.
128. C.-M. Chan, C. Xu et al., “Rq-rag: Learning to refine queries for retrieval augmented generation,” arXiv:2404.00610, 2024. find
129. A. Tayal and A. Tyagi, “Dynamic contexts for generating suggestion questions in rag based conversational systems,” in WWW’24 Companion, 2024. find
130. M. Xia, S. Malladi, S. Gururangan et al., “LESS: selecting influential data for targeted instruction tuning,” arXiv:2402.04333, 2024.
131. A.-L. Bornea, F. Ayed et al., “Telco-rag: Navigating the challenges of retrieval-augmented language models for telecommunications,” arXiv:2404.15939, 2024. — [arXiv:2404.15939](https://arxiv.org/abs/2404.15939) find
132. S. Yao, J. Zhao, D. Yu et al., “React: Synergizing reasoning and acting in language models,” in ICLR, 2023.
133. J. Wei, X. Wang, D. Schuurmans et al., “Chain-of-thought prompting elicits reasoning in large language models,” in NeurIPS, 2022.
134. T. Pouplin, H. Sun, S. Holt, and M. Van der Schaar, “Retrieval-augmented thought process as sequential decision making,” arXiv:2402.07812, 2024. — [arXiv:2402.07812](https://arxiv.org/abs/2402.07812) find
135. J. Liu, “LlamaIndex,” 11 2022. [Online]. Available: https://github.com/jerryjliu/llama index ↗
136. P. Sarthi, S. Abdullah, A. Tuli et al., “Raptor: Recursive abstractive processing for tree-organized retrieval,” in ICLR, 2023.
137. B. Kang, J. Kim et al., “Prompt-rag: Pioneering vector embedding-free retrieval-augmented generation in niche domains, exemplified by korean medicine,” arXiv:2401.11246, 2024. — [arXiv:2401.11246](https://arxiv.org/abs/2401.11246) find
138. V. Raina et al., “Question-based retrieval using atomic units for enterprise rag,” arXiv:2405.12363, 2024. find
139. S. Xiao, Z. Liu, P. Zhang et al., “C-pack: Packaged resources to advance general chinese embedding,” arxiv:2309.07597, 2023.
140. J. Chen, S. Xiao, P. Zhang et al., “Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation,” arxiv:2309.07597, 2023.
141. S. Xiao, Z. Liu, P. Zhang, and X. Xing, “Lm-cocktail: Resilient tuning of language models via model merging,” arxiv:2311.13534, 2023. find
142. P. Zhang, S. Xiao, Z. Liu, Z. Dou, and J.-Y. Nie, “Retrieve anything to augment large language models,” arxiv:2310.07554, 2023. — [arXiv:2310.07554](https://arxiv.org/abs/2310.07554) find
143. M. Kulkarni, P. Tangarajan, K. Kim et al., “Reinforcement learning for optimizing RAG for domain chatbots,” arXiv:2401.06800, 2024. — [arXiv:2401.06800](https://arxiv.org/abs/2401.06800) find
144. W. Wang, Y. Wang et al., “Rap-gen: Retrieval-augmented patch generation with codet5 for automatic program repair,” in ESEC/FSE, 2023. find
145. K. Sawarkar, A. Mangal et al., “Blended rag: Improving rag (retriever-augmented generation) accuracy with semantic search and hybrid query-based retrievers,” arXiv:2404.07220, 2024. — [arXiv:2404.07220](https://arxiv.org/abs/2404.07220) find
146. S.-Q. Yan, J.-C. Gu, Y. Zhu, and Z.-H. Ling, “Corrective retrieval augmented generation,” arXiv:2401.15884, 2024.
147. W. Huang, M. Lapata, P. Vougiouklis et al., “Retrieval augmented generation with rich answer encoding,” in IJCNLP-AACL, 2023. find
148. H. Wang, W. Huang, Y. Deng et al., “Unims-rag: A unified multi-source retrieval-augmented generation for personalized dialogue systems,” arXiv:2401.13256, 2024. — [arXiv:2401.13256](https://arxiv.org/abs/2401.13256) find
149. S. Koley, A. K. Bhunia et al., “You’ll never walk alone: A sketch and text duet for fine-grained image retrieval,” in CVPR, 2024. find
150. M. R. Glass, G. Rossiello, M. F. M. Chowdhury et al., “Re2g: Retrieve, rerank, generate,” in NAACL, 2022.
151. R. F. Nogueira and K. Cho, “Passage re-ranking with BERT,” arxiv:1901.04085, 2019.
152. J. Li, Y. Zhao, Y. Li et al., “Acecoder: Utilizing existing code to enhance code generation,” arXiv:2303.17780, 2023. — [arXiv:2303.17780](https://arxiv.org/abs/2303.17780) find
153. P. Shi, R. Zhang, H. Bai, and J. Lin, “XRICL: cross-lingual retrieval-augmented in-context learning for cross-lingual text-to-sql semantic parsing,” in EMNLP Findings, 2022. find
154. K. Rangan and Y. Yin, “A fine-tuning enhanced rag system with quantized influence measure as ai judge,” arXiv:2402.17081, 2024. — [arXiv:2402.17081](https://arxiv.org/abs/2402.17081) find
155. J. Saad-Falcon, O. Khattab, K. Santhanam et al., “Udapdr: Unsupervised domain adaptation via llm prompting and distillation of rerankers,” in EMNLP, 2023. find
156. L. Wang, N. Yang, and F. Wei, “Learning to retrieve in-context examples for large language models,” arXiv:2307.07164, 2023.
157. P. Finardi, L. Avila et al., “The chronicles of rag: The retriever, the chunk and the generator,” arXiv:2401.07883, 2024. — [arXiv:2401.07883](https://arxiv.org/abs/2401.07883) find
158. J. Li, Y. Yuan, and Z. Zhang, “Enhancing llm factual accuracy with rag to counter hallucinations: A case study on domain-specific queries in private knowledge-bases,” arXiv:2403.10446, 2024. — [arXiv:2403.10446](https://arxiv.org/abs/2403.10446) find
159. Z. Wang, J. Araki, Z. Jiang et al., “Learning to filter context for retrieval-augmented generation,” arxiv:2311.08377, 2023.
160. S. Hofstätter, J. Chen, K. Raman, and H. Zamani, “Fid-light: Efficient and effective retrieval-augmented text generation,” in SIGIR, 2023. find
161. D. Arora, A. Kini, S. R. Chowdhury et al., “Gar-meets-rag paradigm for zero-shot information retrieval,” arXiv:2310.20158, 2023. — [arXiv:2310.20158](https://arxiv.org/abs/2310.20158) find
162. https://www.pinecone.io. ↗
163. W. Yu, D. Iter et al., “Generate rather than retrieve: Large language models are strong context generators,” arXiv:2209.10063, 2022.
164. A. Abdallah and A. Jatowt, “Generator-retriever-generator: A novel approach to open-domain question answering,” arXiv:2307.11278, 2023. — [arXiv:2307.11278](https://arxiv.org/abs/2307.11278) find
165. M. Besta, A. Kubicek et al., “Multi-head rag: Solving multi-aspect problems with llms,” arXiv:2406.05085, 2024. — [arXiv:2406.05085](https://arxiv.org/abs/2406.05085) find
166. E. Saravia, “Prompt Engineering Guide,” https://github.com/dair-ai/Prompt-Engineering-Guide, 12 2022. ↗
167. H. S. Zheng, S. Mishra et al., “Take a step back: Evoking reasoning via abstraction in large language models,” arxiv:2310.06117, 2023. find
168. S. Diao, P. Wang, Y. Lin, and T. Zhang, “Active prompting with chain-of-thought for large language models,” arxiv:2302.12246, 2023. find
169. H. Jiang, Q. Wu, C. Lin et al., “Llmlingua: Compressing prompts for accelerated inference of large language models,” in EMNLP, 2023.
170. N. F. Liu, K. Lin, J. Hewitt et al., “Lost in the middle: How language models use long contexts,” arxiv:2307.03172, 2023.
171. T. Ahmed, K. S. Pai, P. Devanbu, and E. T. Barr, “Automatic semantic augmentation of language model prompts (for code summarization),” arXiv:2304.06815, 2024. — [arXiv:2304.06815](https://arxiv.org/abs/2304.06815) find
172. Z. Xu, Z. Liu, Y. Liu et al., “Activerag: Revealing the treasures of knowledge via active learning,” arXiv:2402.13547, 2024. — [arXiv:2402.13547](https://arxiv.org/abs/2402.13547) find
173. E. Nijkamp, B. Pang, H. Hayashi et al., “A conversational paradigm for program synthesis,” arxiv:2203.13474, 2022. find
174. Y. He, M. Xia, H. Chen et al., “Animate-a-story: Storytelling with retrieval-augmented video generation,” arXiv:2307.06940, 2023. — [arXiv:2307.06940](https://arxiv.org/abs/2307.06940) find
175. E. J. Hu, Y. Shen, P. Wallis et al., “Lora: Low-rank adaptation of large language models,” in ICLR, 2022.
176. C. Liu, P. Çetin, Y. Patodia et al., “Automated code editing with search-generate-modify,” arXiv:2306.06490, 2023. — [arXiv:2306.06490](https://arxiv.org/abs/2306.06490) find
177. H. Joshi, J. P. C. Sánchez, S. Gulwani et al., “Repair is nearly generation: Multilingual program repair with llms,” in AAAI, 2023. find
178. Z. Jiang, F. F. Xu, L. Gao et al., “Active retrieval augmented generation,” arXiv:2305.06983, 2023.
179. A. Mallen, A. Asai, V. Zhong et al., “When not to trust language models: Investigating effectiveness of parametric and non-parametric memories,” in ACL, 2023. find
180. Z. Jiang, J. Araki, H. Ding, and G. Neubig, “How can we know When language models know? on the calibration of language models for question answering,” TACL, 2021. find
181. N. Kandpal, H. Deng, A. Roberts et al., “Large language models struggle to learn long-tail knowledge,” in ICML, 2023.
182. R. Ren, Y. Wang, Y. Qu et al., “Investigating the factual knowledge boundary of large language models with retrieval augmentation,” arxiv:2307.11019, 2023.
183. Y. Wang, P. Li, M. Sun, and Y. Liu, “Self-knowledge guided retrieval augmentation for large language models,” in EMNLP Findings, 2023.
184. H. Ding, L. Pang, Z. Wei et al., “Retrieve only when it needs: Adaptive retrieval augmentation for hallucination mitigation in large language models,” arXiv:2402.10612, 2024. — [arXiv:2402.10612](https://arxiv.org/abs/2402.10612) find
185. S. Jeong, J. Baek, S. Cho et al., “Adaptive-rag: Learning to adapt retrieval-augmented large language models through question complexity,” arXiv:2403.14403, 2024.
186. F. Zhang, B. Chen et al., “Repocoder: Repository-level code completion through iterative retrieval and generation,” in EMNLP, 2023. find
187. Z. Shao, Y. Gong, Y. Shen et al., “Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy,” in EMNLP Findings, 2023.
188. X. Cheng, D. Luo, X. Chen et al., “Lift yourself up: Retrieval-augmented text generation with self-memory,” in NeurIPS, 2023. — Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory
189. Z. Wang, A. Liu, H. Lin et al., “Rat: Retrieval augmented thoughts elicit context-aware reasoning in long-horizon generation,” arXiv:2403.05313, 2024.
190. O. Agarwal, H. Ge, S. Shakeri, and R. Al-Rfou, “Knowledge graph based synthetic corpus generation for knowledge-enhanced language model pre-training,” in NAACL-HLT, 2021.
191. J. Sun, C. Xu, L. Tang et al., “Think-on-graph: Deep and responsible reasoning of large language model with knowledge graph,” arXiv:2307.07697, 2023. find
192. P. Limkonchotiwat, W. Ponwitayarat, C. Udomcharoenchaikit et al., “Cl-relkt: Cross-lingual language knowledge transfer for multilingual retrieval question answering,” in NAACL Findings, 2022. find
193. A. Asai, X. Yu, J. Kasai, and H. Hajishirzi, “One question answering model for many languages with cross-lingual dense passage retrieval,” in NeurIPS, 2021. find
194. K. Lee, S. Han et al., “When to read documents or QA history: On unified and selective open-domain QA,” in ACL Findings, 2023. find
195. S. Yue, W. Chen et al., “Disc-lawllm: Fine-tuning large language models for intelligent legal services,” arXiv:2309.11325, 2023. find
196. S. Siriwardhana, R. Weerasekera, T. Kaluarachchi et al., “Improving the domain adaptation of retrieval augmented generation (RAG) models for open domain question answering,” TACL, vol. 11, pp. 1–17, 2023.
197. Y. Tang and Y. Yang, “Multihop-rag: Benchmarking retrieval-augmented generation for multi-hop queries,” arXiv:2401.15391, 2024.
198. K. Huang, C. Zhai, and H. Ji, “CONCRETE: improving cross-lingual fact-checking with cross-lingual retrieval,” in COLING, 2022. find
199. L. Hagström, D. Saynova, T. Norlund et al., “The effect of scaling, retrieval augmentation and form on the factual consistency of language models,” arXiv:2311.01307, 2023. find
200. H. Zamani and M. Bendersky, “Stochastic rag: End-to-end retrieval-augmented generation through expected utility maximization,” arXiv:2405.02816, 2024. — [arXiv:2405.02816](https://arxiv.org/abs/2405.02816) find
201. Y. Liu, Y. Wan et al., “KG-BART: knowledge graph-augmented BART for generative commonsense reasoning,” in AAAI, 2021. find
202. A. Wan, E. Wallace, and D. Klein, “What evidence do language models find convincing?” arXiv:2402.11782, 2024. find
203. H. Zhang, Z. Liu et al., “Grounded conversation generation as guided traverses in commonsense knowledge graphs,” in ACL, 2020.
204. D. Cai, Y. Wang, W. Bi et al., “Skeleton-to-response: Dialogue generation guided by retrieval memory,” in NAACL-HLT, 2019. find
205. M. Komeili, K. Shuster, and J. Weston, “Internet-augmented dialogue generation,” in ACL, 2022.
206. K. Shuster, J. Xu et al., “Blenderbot 3: a deployed conversational agent that continually learns to responsibly engage,” arXiv:2208.03188, 2022.
207. S. Kim, J. Y. Jang, M. Jung, and S. Shin, “A model of cross-lingual knowledge-grounded response generation for open-domain dialogue systems,” in EMNLP Findings, 2021. find
208. E. Nie, S. Liang, H. Schmid, and H. Schütze, “Cross-lingual retrieval augmented prompt for low-resource languages,” in ACL, 2023. find
209. X. Li, E. Nie, and S. Liang, “From classification to generation: Insights into crosslingual retrieval augmented icl,” in NeurIPS, 2023. find
210. W. Li, J. Li, W. Ma, and Y. Liu, “Citation-enhanced generation for llm-based chatbot,” arXiv:2402.16063, 2024. — [arXiv:2402.16063](https://arxiv.org/abs/2402.16063) find
211. D. Cai, Y. Wang, H. Li et al., “Neural machine translation with monolingual translation memory,” in ACL/IJCNLP, 2021. find
212. U. Khandelwal, A. Fan, D. Jurafsky et al., “Nearest neighbor machine translation,” in ICLR, 2021. find
213. X. Du and H. Ji, “Retrieval-augmented generative question answering for event argument extraction,” in EMNLP, 2022. find
214. Y. Gao, Q. Yin, Z. Li et al., “Retrieval-augmented multilingual keyphrase generation with retriever-generator iterative training,” in NAACL Findings, 2022. find
215. J. Zhang, E. J. Yu, Q. Chen et al., “Retrieval-based full-length wikipedia generation for emergent events,” arXiv:2402.18264, 2024. — [arXiv:2402.18264](https://arxiv.org/abs/2402.18264) find
216. R. Fan, Y. Fan, J. Chen et al., “RIGHT: retrieval-augmented generation for mainstream hashtag recommendation,” arxiv:2312.10466, 2023. — [arXiv:2312.10466](https://arxiv.org/abs/2312.10466) find
217. Z. Wang, S. X. Teo et al., “M-rag: Reinforcing large language model performance through retrieval-augmented generation with multiple partitions,” arXiv:2405.16420, 2024. find
218. Y. Wang, H. Le, A. D. Gotmare et al., “Codet5mix: A pretrained mixture of encoder-decoder transformers for code understanding and generation,” 2022. find
219. A. Madaan, S. Zhou, U. Alon et al., “Language models of code are few-shot commonsense learners,” in EMNLP, 2022. find
220. Y. Wang, H. Le, A. Gotmare et al., “Codet5+: Open code large language models for code understanding and generation,” in EMNLP, 2023. find
221. J. Chen, X. Hu, Z. Li et al., “Code search is all you need? improving code suggestions with code search,” in ICSE, 2024. find
222. D. Zan, B. Chen, Y. Gong et al., “Private-library-oriented code generation with large language models,” arXiv:2307.15370, 2023. — [arXiv:2307.15370](https://arxiv.org/abs/2307.15370) find
223. M. Liu, T. Yang, Y. Lou et al., “Codegen4libs: A two-stage approach for library-oriented code generation,” in ASE, 2023. find
224. D. Liao, S. Pan, Q. Huang et al., “Context-aware code generation framework for code repositories: Local, global, and third-party library awareness,” arXiv:2312.05772, 2023. — [arXiv:2312.05772](https://arxiv.org/abs/2312.05772) find
225. J. Li, Y. Li, G. Li et al., “Skcoder: A sketch-based approach for automatic code generation,” in ICSE, 2023. find
226. Q. Gou, Y. Dong, Y. Wu, and Q. Ke, “Rrgcode: Deep hierarchical search-based code generation,” Journal of Systems and Software, vol. 211, p. 111982, 2024. find
227. K. Zhang, J. Li, G. Li et al., “Codeagent: Enhancing code generation with tool-integrated agent systems for real-world repo-level coding challenges,” arXiv:2401.07339, 2024.
228. H. Su, S. Jiang, Y. Lai et al., “Arks: Active retrieval in knowledge soup for code generation,” arXiv:2402.12317, 2024. — [arXiv:2402.12317](https://arxiv.org/abs/2402.12317) find
229. K. Zhang, G. Li, J. Li et al., “Toolcoder: Teach code generation models to use API search tools,” arXiv:2305.04032, 2023. — [arXiv:2305.04032](https://arxiv.org/abs/2305.04032) find
230. S. Liu, Y. Chen, X. Xie et al., “Retrieval-augmented generation for code summarization via hybrid GNN,” in ICLR, 2021. find
231. F. Yamaguchi, N. Golde, D. Arp, and K. Rieck, “Modeling and discovering vulnerabilities with code property graphs,” in S&P, 2014. find
232. Y. Choi, C. Na et al., “Readsum: Retrieval-augmented adaptive transformer for source code summarization,” IEEE Access, 2023. find
233. J. Zhao, X. Chen, G. Yang, and Y. Shen, “Automatic smart contract comment generation via large language models and in-context learning,” IST, vol. 168, p. 107405, 2024. find
234. A. Alokla, W. Gad, W. Nazih et al., “Retrieval-based transformer pseudocode generation,” Mathematics, vol. 10, no. 4, p. 604, 2022. find
235. J. Xu, Z. Cui et al., “Unilog: Automatic logging via LLM and in-context learning,” in ICSE, 2024. find
236. H. Wang, X. Xia et al., “Context-aware retrieval-based deep commit message generation,” TOSEM, vol. 30, no. 4, pp. 56:1–56:30, 2021. find
237. X. Zhu, C. Sha, and J. Niu, “A simple retrieval-based method for code comment generation,” in SANER, 2022. find
238. T. Ye, L. Wu, T. Ma et al., “Tram: A token-level retrieval-augmented mechanism for source code summarization,” arXiv:2305.11074, 2023. find
239. L. Li, B. Liang, L. Chen, and X. Zhang, “Cross-modal retrieval-enhanced code summarization based on joint learning for retrieval and generation,” Available at SSRN 4724884. find
240. D. Drain, C. Hu, C. Wu et al., “Generating code with the help of retrieved template functions and stack overflow answers,” arXiv:2104.05310, 2021. — [arXiv:2104.05310](https://arxiv.org/abs/2104.05310) find
241. A. Eghbali and M. Pradel, “De-hallucinator: Iterative grounding for llm-based code completion,” arXiv:2401.01701, 2024. — [arXiv:2401.01701](https://arxiv.org/abs/2401.01701) find
242. M. Liang, X. Xie, G. Zhang et al., “Repofuse: Repository-level code completion with fused dual context,” arXiv:2402.14323, 2024. — [arXiv:2402.14323](https://arxiv.org/abs/2402.14323) find
243. D. Shrivastava, D. Kocetkov et al., “Repofusion: Training code models to understand your repository,” arXiv:2306.10998, 2023. find
244. W. Sun, H. Li, M. Yan et al., “Revisiting and improving retrieval-augmented deep assertion generation,” in ASE, 2023. find
245. Y. Ding, Z. Wang et al., “Cocomic: Code completion by jointly modeling in-file and cross-file context,” arXiv:2212.10007, 2022.
246. Z. Tang, J. Ge, S. Liu et al., “Domain adaptive code completion via language models and decoupled domain databases,” in ASE, 2023. find
247. Y. Tsai, M. Liu, and H. Ren, “Rtlfixer: Automatically fixing RTL syntax errors with large language models,” arXiv:2311.16543, 2023. — [arXiv:2311.16543](https://arxiv.org/abs/2311.16543) find
248. B. Bogin, S. Gupta, P. Clark et al., “Leveraging code to improve in-context learning for semantic parsing,” arXiv:2311.09519, 2023. find
249. H. Li, J. Zhang, C. Li, and H. Chen, “Resdsql: Decoupling schema linking and skeleton parsing for text-to-sql,” in AAAI, 2023.
250. K. Zhang, X. Lin, Y. Wang et al., “Refsql: A retrieval-augmentation framework for text-to-sql generation,” in EMNLP Findings, 2023. find
251. S. Chang and E. Fosler-Lussier, “Selective demonstrations for cross-domain text-to-sql,” arXiv:2310.06302, 2023. find
252. L. Nan, Y. Zhao, W. Zou et al., “Enhancing text-to-sql capabilities of large language models: A study on prompt design strategies,” in EMNLP Findings, 2023. find
253. X. Zhang, D. Wang, L. Dou et al., “Multi-hop table retrieval for open-domain text-to-sql,” arXiv:2402.10666, 2024. — [arXiv:2402.10666](https://arxiv.org/abs/2402.10666) find
254. H. Li, J. Zhang, H. Liu et al., “Codes: Towards building open-source language models for text-to-sql,” arXiv:2402.16347, 2024. find
255. Z. Jie and W. Lu, “Leveraging training data in few-shot prompting for numerical reasoning,” arXiv:2305.18170, 2023. find
256. M. Gao, J. Li, H. Fei et al., “De-fine: Decomposing and refining visual programs with auto-feedback,” arXiv:2311.12890, 2023. — [arXiv:2311.12890](https://arxiv.org/abs/2311.12890) find
257. Y. Hao, W. Chen, Z. Zhou, and W. Cui, “E&v: Prompting large language models to perform static analysis by pseudo-code execution and verification,” arXiv:2312.08477, 2023. — [arXiv:2312.08477](https://arxiv.org/abs/2312.08477) find
258. Y. Guo, Z. Li et al., “Retrieval-augmented code generation for universal information extraction,” arXiv:2311.02962, 2023. — [arXiv:2311.02962](https://arxiv.org/abs/2311.02962) find
259. G. Pinto, C. de Souza et al., “Lessons from building stackspot ai: A contextualized ai coding assistant,” arXiv:2311.18450, 2024. — [arXiv:2311.18450](https://arxiv.org/abs/2311.18450) find
260. Z. Liu, C. Chen, J. Wang et al., “Testing the limits: Unusual text inputs generation for mobile app crash detection with large language model,” arXiv:2310.15657, 2023. — [arXiv:2310.15657](https://arxiv.org/abs/2310.15657) find
261. K. D. Bollacker, C. Evans et al., “Freebase: a collaboratively created graph database for structuring human knowledge,” in SIGMOD, 2008. find
262. M. Patidar, A. K. Singh, R. Sawhney et al., “Combining transfer learning with in-context learning using blackbox llms for zero-shot knowledge base question answering,” arXiv:2311.08894, 2023. — [arXiv:2311.08894](https://arxiv.org/abs/2311.08894) find
263. Y. Shu and Z. Yu, “Data distribution bottlenecks in grounding language models to knowledge bases,” arXiv:2309.08345, 2023. — [arXiv:2309.08345](https://arxiv.org/abs/2309.08345) find
264. D. Leake and D. J. Crandall, “On bringing case-based reasoning methodology to deep learning,” in ICCBR, 2020. find
265. L. Zhang, J. Zhang et al., “FC-KBQA: A fine-to-coarse composition framework for knowledge base question answering,” in ACL, 2023. find
266. J. Jiang, K. Zhou et al., “Structgpt: A general framework for large language model to reason over structured data,” in EMNLP, 2023. find
267. J. Baek, A. F. Aji, and A. Saffari, “Knowledge-augmented language model prompting for zero-shot knowledge graph question answering,” arXiv:2306.04136, 2023.
268. P. Sen, S. Mavadia, and A. Saffari, “Knowledge graph-augmented language models for complex question answering,” in NLRSE, 2023. find
269. Y. Wu, N. Hu, S. Bi et al., “Retrieve-rewrite-answer: A kg-to-text enhanced llms framework for knowledge graph question answering,” arXiv:2309.11206, 2023. find
270. C. Wang, Y. Xu, Z. Peng et al., “keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM,” arXiv:2401.00426, 2024. — [arXiv:2401.00426](https://arxiv.org/abs/2401.00426) find
271. J. Liu, S. Cao, J. Shi et al., “Probing structured semantics understanding and generation of language models via question answering,” arXiv:2401.05777, 2024. — [arXiv:2401.05777](https://arxiv.org/abs/2401.05777) find
272. G. Xiong, J. Bao, and W. Zhao, “Interactive-kbqa: Multi-turn interactions for knowledge base question answering with large language models,” arXiv:2402.15131, 2024. find
273. S. Chen, Q. Liu, Z. Yu et al., “Retrack: A flexible and efficient framework for knowledge base question answering,” in ACL, 2021. find
274. D. Yu, C. Zhu, Y. Fang et al., “Kg-fid: Infusing knowledge graph in fusion-in-decoder for open-domain question answering,” in ACL, 2022.
275. M. Ju, W. Yu, T. Zhao et al., “Grape: Knowledge graph enhanced passage reader for open-domain question answering,” in EMNLP Findings, 2022. find
276. Z. Hu, Y. Xu, W. Yu et al., “Empowering language models with knowledge graph reasoning for open-domain question answering,” in EMNLP, 2022. find
277. Q. Yang, Q. Chen, W. Wang et al., “Enhancing multi-modal multi-hop question answering via structured knowledge and unified retrieval-generation,” in MM, 2023. find
278. W. Zhao, Y. Liu, T. Niu et al., “DIVKNOWQA: assessing the reasoning ability of llms via open-domain question answering over knowledge base and text,” arXiv:2310.20170, 2023. find
279. X. Wang, Q. Yang, Y. Qiu et al., “Knowledgpt: Enhancing large language models with retrieval and storage access on knowledge bases,” arXiv:2308.11761, 2023. — [arXiv:2308.11761](https://arxiv.org/abs/2308.11761) find
280. S. Ko, H. Cho, H. Chae et al., “Evidence-focused fact summarization for knowledge-augmented zero-shot question answering,” arXiv:2403.02966, 2024. find
281. Y. Gao, L. Qiao, Z. Kan et al., “Two-stage generative question answering on temporal knowledge graph using large language models,” arXiv:2402.16568, 2024. find
282. T. Guo, Q. Yang, C. Wang et al., “Knowledgenavigator: Leveraging large language models for enhanced reasoning over knowledge graph,” arXiv:2312.15880, 2023. — [arXiv:2312.15880](https://arxiv.org/abs/2312.15880) find
283. C. Mavromatis and G. Karypis, “Gnn-rag: Graph neural retrieval for large language model reasoning,” arXiv:2405.20139, 2024. find
284. S. Min, J. Boyd-Graber, C. Alberti et al., “Neurips 2020 efficientqa competition: Systems, analyses and lessons learned,” in NeurIPS 2020 Competition and Demonstration Track, 2021. find
285. A. H. Li, P. Ng, P. Xu et al., “Dual reader-parser on hybrid textual and tabular evidence for open domain question answering,” in ACL/IJCNLP, 2021. — Open-Domain Question Answering
286. K. Ma, H. Cheng, X. Liu et al., “Open-domain question answering via chain of reasoning over heterogeneous knowledge,” in EMNLP Findings, 2022. find
287. P. Christmann, R. S. Roy, and G. Weikum, “Conversational question answering on heterogeneous sources,” in SIGIR, 2022. find
288. E. Park, S.-M. Lee et al., “Rink: reader-inherited evidence reranker for table-and-text open domain question answering,” in AAAI, 2023. find
289. W. Zhao, Y. Liu, Y. Wan et al., “Localize, retrieve and fuse: A generalized framework for free-form question answering over tables,” arXiv:2309.11049, 2023. find
290. F. Pan, M. Canim et al., “End-to-end table question answering via retrieval-augmented generation,” arXiv:2203.16714, 2022. — [arXiv:2203.16714](https://arxiv.org/abs/2203.16714) find
291. Z. Jiang, Y. Mao, P. He et al., “Omnitab: Pretraining with natural and synthetic data for few-shot table-based question answering,” in NAACL, 2022. find
292. W. Zhong, J. Huang, Q. Liu et al., “Reasoning over hybrid chain for table-and-text open domain question answering,” in IJCAI, 2022. find
293. A. S. Sundar and L. Heck, “ctbl: Augmenting large language models for conversational tables,” arXiv:2303.12024, 2023. — [arXiv:2303.12024](https://arxiv.org/abs/2303.12024) find
294. D. Min, N. Hu, R. Jin et al., “Exploring the impact of table-to-text methods on augmenting llm-based question answering with domain hybrid data,” arXiv:2402.12869, 2024. find
295. S. Roychowdhury, M. Krema et al., “Eratta: Extreme rag for table to answers with large language models,” arXiv:2405.03963, 2024. — [arXiv:2405.03963](https://arxiv.org/abs/2405.03963) find
296. S. Wu, Y. Li, D. Zhang, and Z. Wu, “Improving knowledge-aware dialogue response generation by using human-written prototype dialogues,” in EMNLP Findings, 2020. find
297. M. Kang, J. M. Kwak et al., “Knowledge-consistent dialogue generation with knowledge graphs,” in ICML Workshop, 2022. find
298. Z. Ji, Z. Liu, N. Lee et al., “RHO: reducing hallucination in open-domain dialogues with knowledge grounding,” in ACL Findings, 2023. find
299. J. Baek, N. Chandrasekaran, S. Cucerzan et al., “Knowledge-augmented large language models for personalized contextual query suggestion,” arXiv:2311.06318, 2023. — [arXiv:2311.06318](https://arxiv.org/abs/2311.06318) find
300. X. He, Y. Tian, Y. Sun et al., “G-retriever: Retrieval-augmented generation for textual graph understanding and question answering,” arXiv:2402.07630, 2024.
301. M. M. Hussien, A. N. Melo et al., “Rag-based explainable prediction of road users behaviors for automated driving using knowledge graphs and large language models,” arXiv:2405.00449, 2024. — [arXiv:2405.00449](https://arxiv.org/abs/2405.00449) find
302. B. J. Gutiérrez, Y. Shu et al., “Hipporag: Neurobiologically inspired long-term memory for large language models,” arXiv:2405.14831, 2024.
303. Y. Kirstain, O. Levy, and A. Polyak, “X&fuse: Fusing visual information in text-to-image generation,” arXiv:2303.01000, 2023. find
304. P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” NeurIPS, 2021. find
305. L. Yang, Z. Yu, C. Meng et al., “Mastering text-to-image diffusion: Recaptioning, planning, and generating with multimodal llms,” arXiv:2401.11708, 2024. find
306. Z. Zhang, A. Zhang, M. Li et al., “Multimodal chain-of-thought reasoning in language models,” arXiv:2302.00923, 2023.
307. C. Xu, M. Yang, X. Ao et al., “Retrieval-enhanced adversarial training with dynamic memory-augmented attention for image paragraph captioning,” Knowledge-Based Systems, vol. 214, p. 106730, 2021. find
308. R. Ramos, D. Elliott, and B. Martins, “Retrieval-augmented image captioning,” in EACL, 2023. find
309. Z. Hu, A. Iscen, C. Sun et al., “Reveal: Retrieval-augmented visual-language pre-training with multi-source multimodal knowledge memory,” in CVPR, 2023. find
310. Z. Li, W. Zhao, X. Du et al., “Cross-modal retrieval and semantic refinement for remote sensing image captioning,” Remote Sensing, vol. 16, no. 1, p. 196, 2024. find
311. Z. Yang, Z. Gan, J. Wang et al., “An empirical study of gpt-3 for few-shot knowledge-based vqa,” in AAAI, 2022. find
312. W. Lin and B. Byrne, “Retrieval augmented visual question answering with outside knowledge,” in EMNLP, 2022. find
313. A. Fan, C. Gardent, C. Braud, and A. Bordes, “Augmenting transformers with knn-based composite memory for dialog,” TACL, vol. 9, pp. 82–99, 2021.
314. Z. Liang, H. Hu, C. Xu et al., “Maria: A visual experience powered conversational agent,” in ACL-IJCNLP, 2021. find
315. Q. Fang and Y. Feng, “Neural machine translation with phrase-level universal visual representations,” in ACL, 2022. find
316. S. Whitehead, H. Ji, M. Bansal et al., “Incorporating background knowledge into video description generation,” in EMNLP, 2018. find
317. C. Yin, J. Tang, Z. Xu, and Y. Wang, “Memory augmented deep recurrent neural network for video question answering,” TNNLS, vol. 31, no. 9, pp. 3159–3167, 2019. find
318. J. Pan, Z. Lin, Y. Ge et al., “Retrieving-to-answer: Zero-shot video question answering with frozen large language models,” in ICCV, 2023. find
319. J. Lei, L. Yu, T. L. Berg, and M. Bansal, “Tvqa+: Spatio-temporal grounding for video question answering,” in ACL, 2020. find
320. H. Le, N. Chen, and S. Hoi, “Vgnmn: Video-grounded neural module networks for video-grounded dialogue systems,” in NAACL, 2022. find
321. Z. Wang, M. Li, R. Xu et al., “Language models with image descriptors are strong few-shot video-language learners,” in NeurIPS, 2022. find
322. J. Yuan, S. Sun, D. Omeiza et al., “Rag-driver: Generalisable driving explanations with retrieval-augmented in-context learning in multimodal large language model,” arXiv:2402.10828, 2024. — [arXiv:2402.10828](https://arxiv.org/abs/2402.10828) find
323. S. Ghosh, S. Kumar, C. K. R. Evuru et al., “Recap: retrieval-augmented audio captioning,” in ICASSP, 2024. find
324. B. Elizalde, S. Deshmukh, and H. Wang, “Natural language supervision for general-purpose audio representations,” in ICASSP, 2024. find
325. T. Kouzelis and V. Katsouros, “Weakly-supervised automated audio captioning via text only training,” in DCASE Workshop, 2023. find
326. S. Deshmukh, B. Elizalde, D. Emmanouilidou et al., “Training audio captioning models without audio,” in ICASSP, 2024. find
327. Z. Wang, C. Lu, Y. Wang et al., “Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation,” in NeurIPS, 2024. find
328. L. Yang, Z. Huang, X. Zhou et al., “Prompt-based 3d molecular diffusion models for structure-based drug design,” 2023. find
329. T. Truong Jr and T. Bepler, “Poet: A generative model of protein families as sequences-of-sequences,” NeurIPS, 2024. find
330. G. Frisoni, M. Mizutani, G. Moro, and L. Valgimigli, “Bioreader: a retrieval-enhanced text-to-text transformer for biomedical literature,” in EMNLP, 2022. find
331. X. Yang, M. Ye, Q. You et al., “Writing by memorizing: Hierarchical retrieval-based medical report generation,” arXiv:2106.06471, 2021. find
332. J. Kim and M. Min, “From rag to qa-rag: Integrating generative ai for pharmaceutical regulatory compliance process,” arXiv:2402.01717, 2024. find
333. Y. Ji, Z. Li et al., “Rag-rlrc-laysum at biolaysumm: Integrating retrieval-augmented generation and readability control for layman summarization of biomedical texts,” arXiv:2405.13179, 2024. find
334. K. Yang, A. Swope et al., “Leandojo: Theorem proving with retrieval-augmented language models,” in NeurIPS, 2024.
335. Z. Levonian, C. Li, W. Zhu et al., “Retrieval-augmented generation to improve math question-answering: Trade-offs between groundedness and human preference,” arXiv:2310.03184, 2023. — [arXiv:2310.03184](https://arxiv.org/abs/2310.03184) find
336. J. Chen, H. Lin, X. Han, and L. Sun, “Benchmarking large language models in retrieval-augmented generation,” arxiv:2309.01431, 2023.
337. S. ES, J. James, L. E. Anke, and S. Schockaert, “RAGAS: automated evaluation of retrieval augmented generation,” arxiv:2309.15217, 2023.
338. J. Saad-Falcon, O. Khattab, C. Potts et al., “ARES: an automated evaluation framework for retrieval-augmented generation systems,” arxiv:2311.09476, 2023. find
339. https://github.com/truera/trulens. ↗
340. Y. Lyu, Z. Li, S. Niu et al., “CRUD-RAG: A comprehensive chinese benchmark for retrieval-augmented generation of large language models,” arxiv:2401.17043, 2024. — [arXiv:2401.17043](https://arxiv.org/abs/2401.17043) find
341. G. Xiong, Q. Jin, Z. Lu, and A. Zhang, “Benchmarking retrieval-augmented generation for medicine,” arXiv:2402.13178, 2024.
342. F. Petroni, A. Piktus et al., “Kilt: a benchmark for knowledge intensive language tasks,” in NAACL-HLT, 2021.
343. S. Barnett, S. Kurniawan, S. Thudumu et al., “Seven failure points when engineering a retrieval augmented generation system,” arXiv:2401.05856, 2024. — [arXiv:2401.05856](https://arxiv.org/abs/2401.05856) find
344. F. Cuconasu, G. Trappolini, F. Siciliano et al., “The power of noise: Redefining retrieval for RAG systems,” arXiv:2401.14887, 2024.
345. L. Qiu, P. Shaw, P. Pasupat et al., “Evaluating the impact of model scale for compositional generalization in semantic parsing,” arXiv:2205.12253, 2022. find
346. R. Jagerman, H. Zhuang, Z. Qin et al., “Query expansion by prompting large language models,” arxiv:2305.03653, 2023. — [arXiv:2305.03653](https://arxiv.org/abs/2305.03653) find
347. H. Zhang, P. Zhao, X. Miao et al., “Experimental analysis of large-scale learnable vector storage compression,” VLDB, 2023. find
348. R. Aksitov, C. Chang, D. Reitter et al., “Characterizing attribution and fluency tradeoffs for retrieval-augmented large language models,” arXiv:2302.05578, 2023. — [arXiv:2302.05578](https://arxiv.org/abs/2302.05578) find
349. C. Han, Q. Wang, W. Xiong et al., “Lm-infinite: Simple on-the-fly length generalization for large language models,” arXiv:2308.16137, 2023. — [arXiv:2308.16137](https://arxiv.org/abs/2308.16137) find
350. H. Chase, “Langchain,” https://github.com/langchain-ai/langchain, 2022. ↗
351. W. Jiang, S. Zhang, B. Han et al., “Piperag: Fast retrieval-augmented generation via algorithm-system co-design,” arXiv:2403.05676, 2024. — [arXiv:2403.05676](https://arxiv.org/abs/2403.05676) find
352. K. Meduri et al., “Efficient rag framework for large-scale knowledge bases,” 2024. find
353. S. Jindal, “Did google gemini 1.5 really kill rag?” https://analyticsindiamag.com/did-google-gemini-1-5-really-kill-rag/, 2024. ↗

Updated 31 October 2026

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/zhao-2024-retrieval-augmented-generation-ai-generated/