Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM

J. Ma, Z. Zhao, X. Yi, J. Chen, L. Hong, and E. H. Chi, “Modeling task relationships in multi-task learning with multi-gate mixture-of-experts,” in Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining , 2018, pp. 1930–1939

1939

Earlier work this paper cites.

G. Salton, A. Wong, and C.-S. Yang, “A vector space model for automatic indexing,” Communications of the ACM , vol. 18, no. 11, pp. 613–620, 1975

1975

Earlier work this paper cites.

S. E. Robertson and K. S. Jones, “Relevance weighting of search terms,” Journal of the American Society for Information science , vol. 27, no. 3, pp. 129–146, 1976

1976

Earlier work this paper cites.

M. Dash and H. Liu, “Consistency-based search in feature selection,” Artificial intelligence , vol. 151, no. 1-2, pp. 155–176, 2003

2003

Earlier work this paper cites.

A. Ntoulas, J. Cho, and C. Olston, “What’s new on the web? the evolution of the web from a search engine perspective,” in Proceedings of the 13th international conference on World Wide Web , 2004, pp. 1–12

2004

Earlier work this paper cites.

S. Robertson, H. Zaragoza et al. , “The probabilistic relevance framework: Bm25 and beyond,” Foundations and Trends® in Information Retrieval , vol. 3, no. 4, pp. 333–389, 2009

2009

Earlier work this paper cites.

J. Guo, Y. Fan, Q. Ai, and W. B. Croft, “A deep relevance matching model for ad-hoc retrieval,” in Proceedings of the 25th ACM international on conference on information and knowledge management , 2016, pp. 55–64

2016

Earlier work this paper cites.

Z. Dai, C. Xiong, J. Callan, and Z. Liu, “Convolutional neural networks for soft-matching n-grams in ad-hoc search,” in Proceedings of the eleventh ACM international conference on web search and data mining , 2018, pp. 126–134

2018

Earlier work this paper cites.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018

Original

2018

Earlier work this paper cites.

Z. Dai and J. Callan, “Deeper text understanding for ir with contextual neural language modeling,” in Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval , 2019, pp. 985–988

2019

Earlier work this paper cites.

N. Reimers and I. Gurevych, “Sentence-bert: Sentence embeddings using siamese bert-networks,” arXiv preprint arXiv:1908.10084 , 2019

Original

2019

Earlier work this paper cites.

Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “Roberta: A robustly optimized bert pretraining approach,” arXiv preprint arXiv:1907.11692 , 2019

Original

2019

Earlier work this paper cites.

N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in International Conference on Machine Learning . PMLR, 2019, pp. 2790–2799

2019

Earlier work this paper cites.

G. Izacard and E. Grave, “Leveraging passage retrieval with generative models for open domain question answering,” arXiv preprint arXiv:2007.01282 , 2020

Original

2020

Earlier work this paper cites.

Y. Sun, S. Wang, Y. Li, S. Feng, H. Tian, H. Wu, and H. Wang, “Ernie 2.0: A continual pre-training framework for language understanding,” in Proceedings of the AAAI conference on artificial intelligence , vol. 34, no. 05, 2020, pp. 8968–8975

2020

Cited alongside, same era.

R. Wang, D. Tang, N. Duan, Z. Wei, X. Huang, G. Cao, D. Jiang, M. Zhou et al. , “K-adapter: Infusing knowledge into pre-trained models with adapters,” arXiv preprint arXiv:2002.01808 , 2020

Original

2020

Cited alongside, same era.

L. Xiong, C. Xiong, Y. Li, K.-F. Tang, J. Liu, P. Bennett, J. Ahmed, and A. Overwijk, “Approximate nearest neighbor negative contrastive learning for dense text retrieval,” arXiv preprint arXiv:2007.00808 , 2020

Original

2020

Cited alongside, same era.

X. Ma, J. Guo, R. Zhang, Y. Fan, Y. Li, and X. Cheng, “B-prop: bootstrapped pre-training with representative words prediction for ad-hoc retrieval,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2021, pp. 1513–1522

P. Jia, Y. Liu, X. Zhao, X. Li, C. Hao, S. Wang, and D. Yin, “Mill: Mutual verification with large language models for zero-shot query expansion,” arXiv preprint arXiv:2310.19056 , 2023

Original

2023

Closest in time.

L. Wang, N. Yang, and F. Wei, “Query2doc: Query expansion with large language models,” arXiv preprint arXiv:2303.07678 , 2023

Original

2023

Closest in time.

T. Shen, G. Long, X. Geng, C. Tao, T. Zhou, and D. Jiang, “Large language models are strong zero-shot retriever,” arXiv preprint arXiv:2304.14233 , 2023

Original

2023

Closest in time.

M. Alaofi, L. Gallagher, M. Sanderson, F. Scholer, and P. Thomas, “Can generative llms create query variants for test collections? an exploratory study,” in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2023, pp. 1869–1873

2023

Closest in time.