Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks

G. V. Cormack, C. L. Clarke, and S. Buettcher, “Reciprocal rank fusion outperforms condorcet and individual rank learning methods,” in Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval , 2009, pp. 758–759

2009

Earlier work this paper cites.

L. Xia, J. Xu, Y. Lan, J. Guo, and X. Cheng, “Learning maximal marginal relevance model via directly optimizing diversity evaluation measures,” in Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval , 2015, pp. 113–122

2015

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.

R. Litman, O. Anschel, S. Tsiper, R. Litman, S. Mazor, and R. Manmatha, “Scatter: selective context attentional scene text recognizer,” in proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 962–11 972

2020

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.

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.

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.

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.

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.

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.

Y. Zhang, Y. Li, L. Cui, D. Cai, L. Liu, T. Fu, X. Huang, E. Zhao, Y. Zhang, Y. Chen et al. , “Siren’s song in the ai ocean: A survey on hallucination in large language models,” arXiv preprint arXiv:2309.01219 , 2023

Original

2023

Earlier work this paper cites.

Y. Gao, Y. Xiong, X. Gao, K. Jia, J. Pan, Y. Bi, Y. Dai, J. Sun, and H. Wang, “Retrieval-augmented generation for large language models: A survey,” arXiv preprint arXiv:2312.10997 , 2023

Original

2023

Earlier work this paper cites.

R. Anantha, T. Bethi, D. Vodianik, and S. Chappidi, “Context tuning for retrieval augmented generation,” arXiv preprint arXiv:2312.05708 , 2023

Original

2023

Earlier work this paper cites.

Y. Gao, T. Sheng, Y. Xiang, Y. Xiong, H. Wang, and J. Zhang, “Chat-rec: Towards interactive and explainable llms-augmented recommender system,” arXiv preprint arXiv:2303.14524 , 2023

Original

2023

Earlier work this paper cites.

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

2023

Earlier work this paper cites.

D. S. Asudani, N. K. Nagwani, and P. Singh, “Impact of word embedding models on text analytics in deep learning environment: a review,” Artificial intelligence review , vol. 56, no. 9, pp. 10 345–10 425, 2023

2023

Earlier work this paper cites.

W. Peng, G. Li, Y. Jiang, Z. Wang, D. Ou, X. Zeng, E. Chen et al. , “Large language model based long-tail query rewriting in taobao search,” arXiv preprint arXiv:2311.03758 , 2023

Original

2023

Earlier work this paper cites.

Y. Xi, J. Lin, W. Liu, X. Dai, W. Zhang, R. Zhang, R. Tang, and Y. Yu, “A bird’s-eye view of reranking: from list level to page level,” in Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining , 2023, pp. 1075–1083

2023

Earlier work this paper cites.

Z. Feng, X. Feng, D. Zhao, M. Yang, and B. Qin, “Retrieval-generation synergy augmented large language models,” arXiv preprint arXiv:2310.05149 , 2023

Original

2023

Earlier work this paper cites.

G. Kim, S. Kim, B. Jeon, J. Park, and J. Kang, “Tree of clarifications: Answering ambiguous questions with retrieval-augmented large language models,” arXiv preprint arXiv:2310.14696 , 2023

Original

2023

Cited alongside, same era.

Z. Jiang, F. F. Xu, L. Gao, Z. Sun, Q. Liu, J. Dwivedi-Yu, Y. Yang, J. Callan, and G. Neubig, “Active retrieval augmented generation,” arXiv preprint arXiv:2305.06983 , 2023

Original

2023

Cited alongside, same era.

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

Cited alongside, same era.

D. Arora, A. Kini, S. R. Chowdhury, N. Natarajan, G. Sinha, and A. Sharma, “Gar-meets-rag paradigm for zero-shot information retrieval,” arXiv preprint arXiv:2310.20158 , 2023

Original

2023

Cited alongside, same era.

S. J. Semnani, V. Z. Yao, H. C. Zhang, and M. S. Lam, “Wikichat: Stopping the hallucination of large language model chatbots by few-shot grounding on wikipedia,” arXiv preprint arXiv:2305.14292 , 2023

Original

2023

Later among the works it cites.

J. Baek, S. Jeong, M. Kang, J. C. Park, and S. J. Hwang, “Knowledge-augmented language model verification,” arXiv preprint arXiv:2310.12836 , 2023

Original

2023

Later among the works it cites.

W. Shi, S. Min, M. Yasunaga, M. Seo, R. James, M. Lewis, L. Zettlemoyer, and W.-t. Yih, “Replug: Retrieval-augmented black-box language models,” arXiv preprint arXiv:2301.12652 , 2023

Original

2023

Later among the works it cites.

Z. Shao, Y. Gong, Y. Shen, M. Huang, N. Duan, and W. Chen, “Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy,” arXiv preprint arXiv:2305.15294 , 2023

Original

2023

Later among the works it cites.