CARROT: A Learned Cost-Constrained Retrieval Optimization System for RAG

C. Qu, S. Dai, X. Wei, H. Cai, S. Wang, D. Yin, J. Xu, and J. Wen, “Towards completeness-oriented tool retrieval for large language models,” in CIKM , 2024, pp. 1930–1940

1940

Earlier work this paper cites.

Z. Wang, S. X. Teo, J. Ouyang, Y. Xu, and W. Shi, “M-RAG: reinforcing large language model performance through retrieval-augmented generation with multiple partitions,” in ACL , 2024, pp. 1966–1978

1978

Earlier work this paper cites.

P. Auer, “Using confidence bounds for exploitation-exploration trade-offs,” J. Mach. Learn. Res. , vol. 3, pp. 397–422, 2002

2002

Earlier work this paper cites.

C.-Y. Lin, “ROUGE: A package for automatic evaluation of summaries,” in Text Summarization Branches Out , Barcelona, Spain, Jul. 2004, pp. 74–81

2004

Earlier work this paper cites.

G. Ausiello, P. Crescenzi, G. Gambosi, V. Kann, A. Marchetti-Spaccamela, and M. Protasi, Complexity and approximation: Combinatorial optimization problems and their approximability properties . Springer Science & Business Media, 2012

2012

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,” in NeurIPS , ser. CEUR Workshop Proceedings, vol. 1773, 2016

2016

Earlier work this paper cites.

Z. Cao, W. Li, S. Li, and F. Wei, “Retrieve, rerank and rewrite: Soft template based neural summarization,” in ACL , 2018, pp. 152–161

2018

Earlier work this paper cites.

D. Cohen, L. Yang, and W. B. Croft, “Wikipassageqa: A benchmark collection for research on non-factoid answer passage retrieval,” in SIGIR 2018 , 2018, pp. 1165–1168

2018

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,” in EMNLP , 2018

2018

Earlier work this paper cites.

M. Post, “A call for clarity in reporting BLEU scores,” in WMT 2018 , 2018, pp. 186–191

2018

Earlier work this paper cites.

E. I. Obilor and E. C. Amadi, “Test for significance of pearson’s correlation coefficient,” International Journal of Innovative Mathematics, Statistics & Energy Policies , vol. 6, no. 1, pp. 11–23, 2018

2018

Earlier work this paper cites.

O. Khattab and M. Zaharia, “Colbert: Efficient and effective passage search via contextualized late interaction over BERT,” in SIGIR 2020 . ACM, 2020, pp. 39–48

2020

Earlier work this paper cites.

P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan, “Supervised contrastive learning,” in NeurIPS 2020 , 2020

2020

Earlier work this paper cites.

D. M. Powers, “Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation,” arXiv preprint arXiv:2010.16061 , 2020

Original

2020

Earlier work this paper cites.

K. Santhanam, O. Khattab, J. Saad-Falcon, C. Potts, and M. Zaharia, “Colbertv2: Effective and efficient retrieval via lightweight late interaction,” in NAACL 2022 , 2022, pp. 3715–3734

2022

Earlier work this paper cites.

Y. Lu, M. Bartolo, A. Moore, S. Riedel, and P. Stenetorp, “Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity,” in ACL , 2022, pp. 8086–8098

2022

Earlier work this paper cites.

S. Arora, B. Yang, S. Eyuboglu, A. Narayan, A. Hojel, I. Trummer, and C. Ré, “Language models enable simple systems for generating structured views of heterogeneous data lakes,” Proc. VLDB Endow. , vol. 17, no. 2, pp. 92–105, 2023

2023

Earlier work this paper cites.

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,” in EMNLP , 2023

2023

Earlier work this paper cites.

X. Ma, Y. Gong, P. He, H. Zhao, and N. Duan, “Query rewriting in retrieval-augmented large language models,” in EMNLP , 2023

2023

Earlier work this paper cites.

L. Gao, X. Ma, J. Lin, and J. Callan, “Precise zero-shot dense retrieval without relevance labels,” in ACL , 2023, pp. 1762–1777

2023

Earlier work this paper cites.

A. Askari, M. Aliannejadi, C. Meng, E. Kanoulas, and S. Verberne, “Expand, highlight, generate: Rl-driven document generation for passage reranking,” in EMNLP , 2023, pp. 10 087–10 099

2023

Earlier work this paper cites.

X. Li, Y. Zhu, S. Liu, J. Ju, Y. Qu, and G. Cheng, “Dyrren: A dynamic retriever-reranker-generator model for numerical reasoning over tabular and textual data,” in AAAI, 2023 , 2023, pp. 13 139–13 147

2023

Earlier work this paper cites.

R. Zhu, W. Chen, B. Ding, X. Chen, A. Pfadler, Z. Wu, and J. Zhou, “Lero: A learning-to-rank query optimizer,” Proc. VLDB Endow. , vol. 16, no. 6, pp. 1466–1479, 2023

2023

Earlier work this paper cites.

F. Atif, O. E. Khatib, and D. E. Difallah, “Beamqa: Multi-hop knowledge graph question answering with sequence-to-sequence prediction and beam search,” in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval . ACM, 2023, pp. 781–790

2023

Cited alongside, same era.

X. Zhao, X. Zhou, and G. Li, “Chat2data: An interactive data analysis system with rag, vector databases and llms,” Proc. VLDB Endow. , vol. 17, no. 12, pp. 4481–4484, 2024

2024

Cited alongside, same era.

J. Zhu, P. Cai, K. Xu, L. Li, Y. Sun, S. Zhou, H. Su, L. Tang, and Q. Liu, “Autotqa: Towards autonomous tabular question answering through multi-agent large language models,” Proc. VLDB Endow. , vol. 17, no. 12, pp. 3920–3933, 2024

2024

Cited alongside, same era.

P. Li, Y. He, D. Yashar, W. Cui, S. Ge, H. Zhang, D. R. Fainman, D. Zhang, and S. Chaudhuri, “Table-gpt: Table fine-tuned GPT for diverse table tasks,” Proc. ACM Manag. Data , vol. 2, no. 3, p. 176, 2024

2024

S. Jain, X. Ma, A. Deoras, and B. Xiang, “Lightweight reranking for language model generations,” in ACL , 2024, pp. 6960–6984

2024

Closest in time.

M. Wang, H. Wu, X. Ke, Y. Gao, X. Xu, and L. Chen, “An interactive multi-modal query answering system with retrieval-augmented large language models,” Proc. VLDB Endow. , vol. 17, no. 12, pp. 4333–4336, 2024

2024

Closest in time.

T. Li, X. Yang, Y. Ke, B. Wang, Y. Liu, and J. Xu, “Alleviating the inconsistency of multimodal data in cross-modal retrieval,” in ICDE , 2024, pp. 4643–4656

2024

Closest in time.

M. Y. Eltabakh, Z. A. Naeem, M. S. Ahmad, M. Ouzzani, and N. Tang, “Retclean: Retrieval-based tabular data cleaning using llms and data lakes,” Proc. VLDB Endow. , vol. 17, no. 12, pp. 4421–4424, 2024

2024

Closest in time.