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As a primary means of information acquisition, information retrieval (IR) systems, such as search engines, have integrated themselves into our daily lives.
G. Salton, A. Wong, and C. Yang, “A vector space model for automatic indexing,” Commun. ACM , vol. 18, no. 11, pp. 613–620, 1975
1975
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 Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024 , L. Ku, A. Martins, and V. Srikumar, Eds. Association for Computational Linguistics, 2024, pp. 1966–1978
1978
Earlier work this paper cites.
E. A. Fox, “Lexical relations: Enhancing effectiveness of information retrieval systems,” SIGIR Forum , vol. 15, no. 3, pp. 5–36, 1980
1980
Earlier work this paper cites.
G. Salton and M. McGill, Introduction to Modern Information Retrieval . McGraw-Hill Book Company, 1984
1984
Earlier work this paper cites.
H. J. Peat and P. Willett, “The limitations of term co-occurrence data for query expansion in document retrieval systems,” J. Am. Soc. Inf. Sci. , vol. 42, no. 5, pp. 378–383, 1991
1991
Earlier work this paper cites.
S. E. Robertson, S. Walker, S. Jones, M. Hancock-Beaulieu, and M. Gatford, “Okapi at TREC-3,” in Proceedings of The Third Text REtrieval Conference, TREC 1994, Gaithersburg, Maryland, USA, November 2-4, 1994 , ser. NIST Special Publication, D. K. Harman, Ed., vol. 500-225. National Institute of Standards and Technology (NIST), 1994, pp. 109–126
1994
Earlier work this paper cites.
Y. Arens, C. A. Knoblock, and W. Shen, “Query reformulation for dynamic information integration,” J. Intell. Inf. Syst. , vol. 6, no. 2/3, pp. 99–130, 1996
1996
Earlier work this paper cites.
J. G. Carbonell and J. Goldstein, “The use of mmr, diversity-based reranking for reordering documents and producing summaries,” in SIGIR ’98: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, August 24-28 1998, Melbourne, Australia , W. B. Croft, A. Moffat, C. J. van Rijsbergen, R. Wilkinson, and J. Zobel, Eds. ACM, 1998, pp. 335–336
1998
Earlier work this paper cites.
C. Fellbaum, “Wordnet: An electronic lexical database,” MIT Press google schola , vol. 2, pp. 678–686, 1998
1998
Earlier work this paper cites.
F. Song and W. B. Croft, “A general language model for information retrieval,” in Proceedings of the 1999 ACM CIKM International Conference on Information and Knowledge Management, Kansas City, Missouri, USA, November 2-6, 1999 . ACM, 1999, pp. 316–321
1999
Earlier work this paper cites.
S. Gauch, J. Wang, and S. M. Rachakonda, “A corpus analysis approach for automatic query expansion and its extension to multiple databases,” ACM Trans. Inf. Syst. , vol. 17, no. 3, pp. 250–269, 1999
1999
Earlier work this paper cites.
K. Järvelin and J. Kekäläinen, “Cumulated gain-based evaluation of IR techniques,” ACM Trans. Inf. Syst. , vol. 20, no. 4, pp. 422–446, 2002
2002
Earlier work this paper cites.
K. Papineni, S. Roukos, T. Ward, and W. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, July 6-12, 2002, Philadelphia, PA, USA . ACL, 2002, pp. 311–318
2002
Earlier work this paper cites.
O. Weller, K. Lo, D. Wadden, D. J. Lawrie, B. V. Durme, A. Cohan, and L. Soldaini, “When do generative query and document expansions fail? A comprehensive study across methods, retrievers, and datasets,” in Findings of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024 , Y. Graham and M. Purver, Eds. Association for Computational Linguistics, 2024, pp. 1987–2003
2003
Earlier work this paper cites.
Y. Zhu, J. Nie, K. Zhou, P. Du, H. Jiang, and Z. Dou, “Proactive retrieval-based chatbots based on relevant knowledge and goals,” in SIGIR ’21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021 , F. Diaz, C. Shah, T. Suel, P. Castells, R. Jones, and T. Sakai, Eds. ACM, 2021, pp. 2000–2004
2004
Earlier work this paper cites.
C.-Y. Lin, “ROUGE: A package for automatic evaluation of summaries,” in Text Summarization Branches Out . Barcelona, Spain: Association for Computational Linguistics, Jul. 2004, pp. 74–81
2004
Earlier work this paper cites.
J. Teevan, S. T. Dumais, and E. Horvitz, “Personalizing search via automated analysis of interests and activities,” in SIGIR 2005: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Salvador, Brazil, August 15-19, 2005 , R. A. Baeza-Yates, N. Ziviani, G. Marchionini, A. Moffat, and J. Tait, Eds. ACM, 2005, pp. 449–456
2005
Earlier work this paper cites.
X. Liu and W. B. Croft, “Statistical language modeling for information retrieval,” Annu. Rev. Inf. Sci. Technol. , vol. 39, no. 1, pp. 1–31, 2005
2005
Earlier work this paper cites.
C. J. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. N. Hullender, “Learning to rank using gradient descent,” in ICML , ser. ACM International Conference Proceeding Series, vol. 119. ACM, 2005, pp. 89–96
2005
Earlier work this paper cites.
Y. Li, W. P. R. Luk, K. S. E. Ho, and F. L. K. Chung, “Improving weak ad-hoc queries using wikipedia asexternal corpus,” in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval , 2007, pp. 797–798
2007
Earlier work this paper cites.
R. Datta, D. Joshi, J. Li, and J. Z. Wang, “Image retrieval: Ideas, influences, and trends of the new age,” ACM Comput. Surv. , vol. 40, no. 2, pp. 5:1–5:60, 2008
2008
Earlier work this paper cites.
J. Huang and E. N. Efthimiadis, “Analyzing and evaluating query reformulation strategies in web search logs,” in Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China, November 2-6, 2009 , D. W. Cheung, I. Song, W. W. Chu, X. Hu, and J. Lin, Eds. ACM, 2009, pp. 77–86
2009
Earlier work this paper cites.
R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong, “Diversifying search results,” in Proceedings of the Second International Conference on Web Search and Web Data Mining, WSDM 2009, Barcelona, Spain, February 9-11, 2009 , R. Baeza-Yates, P. Boldi, B. A. Ribeiro-Neto, and B. B. Cambazoglu, Eds. ACM, 2009, pp. 5–14
2009
Earlier work this paper cites.
J. Martineau and T. Finin, “Delta TFIDF: an improved feature space for sentiment analysis,” in Proceedings of the Third International Conference on Weblogs and Social Media, ICWSM 2009, San Jose, California, USA, May 17-20, 2009 , E. Adar, M. Hurst, T. Finin, N. S. Glance, N. Nicolov, and B. L. Tseng, Eds. The AAAI Press, 2009
2009
Earlier work this paper cites.
N. Craswell, “Mean reciprocal rank,” in Encyclopedia of Database Systems , L. Liu and M. T. Özsu, Eds. Springer US, 2009, p. 1703
2009
Earlier work this paper cites.
P. N. Bennett, R. W. White, W. Chu, S. T. Dumais, P. Bailey, F. Borisyuk, and X. Cui, “Modeling the impact of short- and long-term behavior on search personalization,” in The 35th International ACM SIGIR conference on research and development in Information Retrieval, SIGIR ’12, Portland, OR, USA, August 12-16, 2012 , W. R. Hersh, J. Callan, Y. Maarek, and M. Sanderson, Eds. ACM, 2012, pp. 185–194
2012
Earlier work this paper cites.
H. Zohar, C. Liebeskind, J. Schler, and I. Dagan, “Automatic thesaurus construction for cross generation corpus,” ACM Journal on Computing and Cultural Heritage , vol. 6, no. 1, pp. 4:1–4:19, 2013
2013
Earlier work this paper cites.
C. Xiong and J. Callan, “Query expansion with freebase,” in Proceedings of the 2015 International Conference on The Theory of Information Retrieval, ICTIR 2015, Northampton, Massachusetts, USA, September 27-30, 2015 , J. Allan, W. B. Croft, A. P. de Vries, and C. Zhai, Eds. ACM, 2015, pp. 111–120
2015
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 Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, USA, October 24-28, 2016 , S. Mukhopadhyay, C. Zhai, E. Bertino, F. Crestani, J. Mostafa, J. Tang, L. Si, X. Zhou, Y. Chang, Y. Li, and P. Sondhi, Eds. ACM, 2016, pp. 55–64
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,” in CoCo@NIPS , ser. CEUR Workshop Proceedings, vol. 1773. CEUR-WS.org, 2016
2016
Earlier work this paper cites.
Y. Wu, W. Wu, C. Xing, M. Zhou, and Z. Li, “Sequential matching network: A new architecture for multi-turn response selection in retrieval-based chatbots,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers , R. Barzilay and M. Kan, Eds. Association for Computational Linguistics, 2017, pp. 496–505
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, and R. Garnett, Eds., 2017, pp. 5998–6008
2017
Earlier work this paper cites.
J. Singh and A. Sharan, “A new fuzzy logic-based query expansion model for efficient information retrieval using relevance feedback approach,” Neural Comput. Appl. , vol. 28, no. 9, pp. 2557–2580, 2017
2017
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,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers , R. Barzilay and M. Kan, Eds. Association for Computational Linguistics, 2017, pp. 1601–1611
2017
Earlier work this paper cites.
H. Shum, X. He, and D. Li, “From eliza to xiaoice: challenges and opportunities with social chatbots,” Frontiers Inf. Technol. Electron. Eng. , vol. 19, no. 1, pp. 10–26, 2018
2018
Earlier work this paper cites.
S. Ge, Z. Dou, Z. Jiang, J. Nie, and J. Wen, “Personalizing search results using hierarchical RNN with query-aware attention,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22-26, 2018 , A. Cuzzocrea, J. Allan, N. W. Paton, D. Srivastava, R. Agrawal, A. Z. Broder, M. J. Zaki, K. S. Candan, A. Labrinidis, A. Schuster, and H. Wang, Eds. ACM, 2018, pp. 347–356
2018
Earlier work this paper cites.
M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, “Deep contextualized word representations,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 1 (Long Papers) , M. A. Walker, H. Ji, and A. Stent, Eds. Association for Computational Linguistics, 2018, pp. 2227–2237
2018
Earlier work this paper cites.
H. Wachsmuth, S. Syed, and B. Stein, “Retrieval of the best counterargument without prior topic knowledge,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers , I. Gurevych and Y. Miyao, Eds. Association for Computational Linguistics, 2018, pp. 241–251
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 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018 , E. Riloff, D. Chiang, J. Hockenmaier, and J. Tsujii, Eds. Association for Computational Linguistics, 2018, pp. 2369–2380
2018
Earlier work this paper cites.
C. Yuan, W. Zhou, M. Li, S. Lv, F. Zhu, J. Han, and S. Hu, “Multi-hop selector network for multi-turn response selection in retrieval-based chatbots,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019 , K. Inui, J. Jiang, V. Ng, and X. Wan, Eds. Association for Computational Linguistics, 2019, pp. 111–120
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language models are unsupervised multitask learners,” 2019
2019
Earlier work this paper cites.
J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers) , J. Burstein, C. Doran, and T. Solorio, Eds. Association for Computational Linguistics, 2019, pp. 4171–4186
2019
Earlier work this paper cites.
H. K. Azad and A. Deepak, “Query expansion techniques for information retrieval: A survey,” Inf. Process. Manag. , vol. 56, no. 5, pp. 1698–1735, 2019
2019
Earlier work this paper cites.
T. Kwiatkowski, J. Palomaki, O. Redfield, M. Collins, A. P. Parikh, C. Alberti, D. Epstein, I. Polosukhin, J. Devlin, K. Lee, K. Toutanova, L. Jones, M. Kelcey, M. Chang, A. M. Dai, J. Uszkoreit, Q. Le, and S. Petrov, “Natural questions: a benchmark for question answering research,” Trans. Assoc. Comput. Linguistics , vol. 7, pp. 452–466, 2019
2019
Earlier work this paper cites.
V. Karpukhin, B. Oguz, S. Min, P. S. H. Lewis, L. Wu, S. Edunov, D. Chen, and W. Yih, “Dense passage retrieval for open-domain question answering,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020 , B. Webber, T. Cohn, Y. He, and Y. Liu, Eds. Association for Computational Linguistics, 2020, pp. 6769–6781
2020
Earlier work this paper cites.
R. Nogueira, Z. Jiang, R. Pradeep, and J. Lin, “Document ranking with a pretrained sequence-to-sequence model,” in Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16-20 November 2020 , ser. Findings of ACL, T. Cohn, Y. He, and Y. Liu, Eds., vol. EMNLP 2020. Association for Computational Linguistics, 2020, pp. 708–718
2020
Earlier work this paper cites.
J. Liu, Z. Dou, X. Wang, S. Lu, and J. Wen, “DVGAN: A minimax game for search result diversification combining explicit and implicit features,” in Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020 , J. X. Huang, Y. Chang, X. Cheng, J. Kamps, V. Murdock, J. Wen, and Y. Liu, Eds. ACM, 2020, pp. 479–488
2020
Earlier work this paper cites.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual , H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer, “BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020 , D. Jurafsky, J. Chai, N. Schluter, and J. R. Tetreault, Eds. Association for Computational Linguistics, 2020, pp. 7871–7880
2020
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” J. Mach. Learn. Res. , vol. 21, pp. 140:1–140:67, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
K. Guu, K. Lee, Z. Tung, P. Pasupat, and M. Chang, “Retrieval augmented language model pre-training,” in Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event , ser. Proceedings of Machine Learning Research, vol. 119. PMLR, 2020, pp. 3929–3938
2020
Earlier work this paper cites.
P. S. H. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. Yih, T. Rocktäschel, S. Riedel, and D. Kiela, “Retrieval-augmented generation for knowledge-intensive NLP tasks,” in Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual , H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., 2020
2020
Earlier work this paper cites.
X. Ho, A. D. Nguyen, S. Sugawara, and A. Aizawa, “Constructing A multi-hop QA dataset for comprehensive evaluation of reasoning steps,” in Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8-13, 2020 , D. Scott, N. Bel, and C. Zong, Eds. International Committee on Computational Linguistics, 2020, pp. 6609–6625
2020
Earlier work this paper cites.
Y. Zhu, J. Nie, K. Zhou, P. Du, and Z. Dou, “Content selection network for document-grounded retrieval-based chatbots,” in Advances in Information Retrieval - 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28 - April 1, 2021, Proceedings, Part I , ser. Lecture Notes in Computer Science, D. Hiemstra, M. Moens, J. Mothe, R. Perego, M. Potthast, and F. Sebastiani, Eds., vol. 12656. Springer, 2021, pp. 755–769
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
Y. Qu, Y. Ding, J. Liu, K. Liu, R. Ren, W. X. Zhao, D. Dong, H. Wu, and H. Wang, “Rocketqa: An optimized training approach to dense passage retrieval for open-domain question answering,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021 , K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tür, I. Beltagy, S. Bethard, R. Cotterell, T. Chakraborty, and Y. Zhou, Eds. Association for Computational Linguistics, 2021, pp. 5835–5847
2021
Earlier work this paper cites.
Y. Zhu, J. Nie, Z. Dou, Z. Ma, X. Zhang, P. Du, X. Zuo, and H. Jiang, “Contrastive learning of user behavior sequence for context-aware document ranking,” in CIKM ’21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021 , G. Demartini, G. Zuccon, J. S. Culpepper, Z. Huang, and H. Tong, Eds. ACM, 2021, pp. 2780–2791
2021
Earlier work this paper cites.
Y. Zhou, Z. Dou, Y. Zhu, and J. Wen, “PSSL: self-supervised learning for personalized search with contrastive sampling,” in CIKM ’21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021 , G. Demartini, G. Zuccon, J. S. Culpepper, Z. Huang, and H. Tong, Eds. ACM, 2021, pp. 2749–2758
2021
Earlier work this paper cites.
Z. Su, Z. Dou, Y. Zhu, X. Qin, and J. Wen, “Modeling intent graph for search result diversification,” in SIGIR ’21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021 , F. Diaz, C. Shah, T. Suel, P. Castells, R. Jones, and T. Sakai, Eds. ACM, 2021, pp. 736–746
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
L. Xiong, C. Xiong, Y. Li, K. Tang, J. Liu, P. N. Bennett, J. Ahmed, and A. Overwijk, “Approximate nearest neighbor negative contrastive learning for dense text retrieval,” in 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021 . OpenReview.net, 2021
2021
Earlier work this paper cites.
J. Lin, R. F. Nogueira, and A. Yates, Pretrained Transformers for Text Ranking: BERT and Beyond , ser. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers, 2021
2021
Earlier work this paper cites.
J. Li, T. Tang, W. X. Zhao, and J. Wen, “Pretrained language model for text generation: A survey,” in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021 , Z. Zhou, Ed. ijcai.org, 2021, pp. 4492–4499
2021
Earlier work this paper cites.
X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021 , C. Zong, F. Xia, W. Li, and R. Navigli, Eds. Association for Computational Linguistics, 2021, pp. 4582–4597
2021
Earlier work this paper cites.
B. Lester, R. Al-Rfou, and N. Constant, “The power of scale for parameter-efficient prompt tuning,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021 , M. Moens, X. Huang, L. Specia, and S. W. Yih, Eds. Association for Computational Linguistics, 2021, pp. 3045–3059
2021
Earlier work this paper cites.
N. Thakur, N. Reimers, A. Rücklé, A. Srivastava, and I. Gurevych, “BEIR: A heterogeneous benchmark for zero-shot evaluation of information retrieval models,” in NeurIPS Datasets and Benchmarks , 2021
2021
Earlier work this paper cites.
D. Metzler, Y. Tay, D. Bahri, and M. Najork, “Rethinking search: making domain experts out of dilettantes,” SIGIR Forum , vol. 55, no. 1, pp. 13:1–13:27, 2021
2021
Earlier work this paper cites.
J. Ju, J. Yang, and C. Wang, “Text-to-text multi-view learning for passage re-ranking,” in SIGIR ’21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021 , F. Diaz, C. Shah, T. Suel, P. Castells, R. Jones, and T. Sakai, Eds. ACM, 2021, pp. 1803–1807
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
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,” Trans. Assoc. Comput. Linguistics , vol. 9, pp. 962–977, 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
S. Borgeaud, A. Mensch, J. Hoffmann, T. Cai, E. Rutherford, K. Millican, G. van den Driessche, J. Lespiau, B. Damoc, A. Clark, D. de Las Casas, A. Guy, J. Menick, R. Ring, T. Hennigan, S. Huang, L. Maggiore, C. Jones, A. Cassirer, A. Brock, M. Paganini, G. Irving, O. Vinyals, S. Osindero, K. Simonyan, J. W. Rae, E. Elsen, and L. Sifre, “Improving language models by retrieving from trillions of tokens,” in International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA , ser. Proceedings of Machine Learning Research, K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvári, G. Niu, and S. Sabato, Eds., vol. 162. PMLR, 2022, pp. 2206–2240
2022
Earlier work this paper cites.
J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler, E. H. Chi, T. Hashimoto, O. Vinyals, P. Liang, J. Dean, and W. Fedus, “Emergent abilities of large language models,” Trans. Mach. Learn. Res. , vol. 2022, 2022
2022
Earlier work this paper cites.
A. Clark, D. de Las Casas, A. Guy, A. Mensch, M. Paganini, J. Hoffmann, B. Damoc, B. A. Hechtman, T. Cai, S. Borgeaud, G. van den Driessche, E. Rutherford, T. Hennigan, M. J. Johnson, A. Cassirer, C. Jones, E. Buchatskaya, D. Budden, L. Sifre, S. Osindero, O. Vinyals, M. Ranzato, J. W. Rae, E. Elsen, K. Kavukcuoglu, and K. Simonyan, “Unified scaling laws for routed language models,” in International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA , ser. Proceedings of Machine Learning Research, K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvári, G. Niu, and S. Sabato, Eds., vol. 162. PMLR, 2022, pp. 4057–4086
2022
Earlier work this paper cites.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” in The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022 . OpenReview.net, 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
K. Mao, Z. Dou, H. Qian, F. Mo, X. Cheng, and Z. Cao, “Convtrans: Transforming web search sessions for conversational dense retrieval,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022 , Y. Goldberg, Z. Kozareva, and Y. Zhang, Eds. Association for Computational Linguistics, 2022, pp. 2935–2946
2022
Earlier work this paper cites.
Z. Dai, A. T. Chaganty, V. Y. Zhao, A. Amini, Q. M. Rashid, M. Green, and K. Guu, “Dialog inpainting: Turning documents into dialogs,” in International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA , ser. Proceedings of Machine Learning Research, K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvári, G. Niu, and S. Sabato, Eds., vol. 162. PMLR, 2022, pp. 4558–4586
2022
Earlier work this paper cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. H. Chi, Q. V. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” in NeurIPS , 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
J. Ni, C. Qu, J. Lu, Z. Dai, G. H. Ábrego, J. Ma, V. Y. Zhao, Y. Luan, K. B. Hall, M. Chang, and Y. Yang, “Large dual encoders are generalizable retrievers,” in EMNLP . Association for Computational Linguistics, 2022, pp. 9844–9855
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
J. Chen, R. Zhang, J. Guo, Y. Liu, Y. Fan, and X. Cheng, “Corpusbrain: Pre-train a generative retrieval model for knowledge-intensive language tasks,” in Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022 , M. A. Hasan and L. Xiong, Eds. ACM, 2022, pp. 191–200
2022
Earlier work this paper cites.
Y. Tay, V. Tran, M. Dehghani, J. Ni, D. Bahri, H. Mehta, Z. Qin, K. Hui, Z. Zhao, J. P. Gupta, T. Schuster, W. W. Cohen, and D. Metzler, “Transformer memory as a differentiable search index,” in NeurIPS , 2022
2022
Earlier work this paper cites.
Y. Wang, Y. Hou, H. Wang, Z. Miao, S. Wu, Q. Chen, Y. Xia, C. Chi, G. Zhao, Z. Liu, X. Xie, H. Sun, W. Deng, Q. Zhang, and M. Yang, “A neural corpus indexer for document retrieval,” in Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022 , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds., 2022
2022
Earlier work this paper cites.
M. Bevilacqua, G. Ottaviano, P. S. H. Lewis, S. Yih, S. Riedel, and F. Petroni, “Autoregressive search engines: Generating substrings as document identifiers,” in Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022 , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds., 2022
2022
Earlier work this paper cites.
D. S. Sachan, M. Lewis, M. Joshi, A. Aghajanyan, W. Yih, J. Pineau, and L. Zettlemoyer, “Improving passage retrieval with zero-shot question generation,” in EMNLP . Association for Computational Linguistics, 2022, pp. 3781–3797
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
K. Shuster, M. Komeili, L. Adolphs, S. Roller, A. Szlam, and J. Weston, “Language models that seek for knowledge: Modular search & generation for dialogue and prompt completion,” in Findings of the Association for Computational Linguistics: EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022 , Y. Goldberg, Z. Kozareva, and Y. Zhang, Eds. Association for Computational Linguistics, 2022, pp. 373–393
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” ACM Comput. Surv. , vol. 55, no. 9, pp. 195:1–195:35, 2023
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
J. Huang and K. C. Chang, “Towards reasoning in large language models: A survey,” in Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada, July 9-14, 2023 , A. Rogers, J. L. Boyd-Graber, and N. Okazaki, Eds. Association for Computational Linguistics, 2023, pp. 1049–1065
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer, “Qlora: Efficient finetuning of quantized llms,” in Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023 , A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, Eds., 2023
2023
Earlier work this paper cites.
L. Wang, N. Yang, and F. Wei, “Query2doc: Query expansion with large language models,” pp. 9414–9423, 2023
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
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, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023 , H. Chen, W. E. Duh, H. Huang, M. P. Kato, J. Mothe, and B. Poblete, Eds. ACM, 2023, pp. 1869–1873
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
K. Mao, Z. Dou, F. Mo, J. Hou, H. Chen, and H. Qian, “Large language models know your contextual search intent: A prompting framework for conversational search,” in Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, December 6-10, 2023 , H. Bouamor, J. Pino, and K. Bali, Eds. Association for Computational Linguistics, 2023, pp. 1211–1225
2023
Earlier work this paper cites.
C. Huang, C. Hsu, T. Hsu, C. Li, and Y. Chen, “CONVERSER: few-shot conversational dense retrieval with synthetic data generation,” in Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2023, Prague, Czechia, September 11 - 15, 2023 , D. Schlangen, S. Stoyanchev, S. Joty, O. Dusek, C. Kennington, and M. Alikhani, Eds. Association for Computational Linguistics, 2023, pp. 381–387
2023
Earlier work this paper cites.
F. Ye, M. Fang, S. Li, and E. Yilmaz, “Enhancing conversational search: Large language model-aided informative query rewriting,” in Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, December 6-10, 2023 , H. Bouamor, J. Pino, and K. Bali, Eds. Association for Computational Linguistics, 2023, pp. 5985–6006
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
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,” in The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net, 2023
2024
Closest in time.
2024
Closest in time.
N. Sinhababu, A. Parry, D. Ganguly, D. Samanta, and P. Mitra, “Few-shot prompting for pairwise ranking: An effective non-parametric retrieval model,” in Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, USA, November 12-16, 2024 . Association for Computational Linguistics, 2024, pp. 12 363–12 377
2024
Closest in time.
M. Rashid, J. Meem, Y. Dong, and V. Hristidis, “EcoRank: Budget-constrained text re-ranking using large language models,” in Findings of the Association for Computational Linguistics ACL 2024 , L.-W. Ku, A. Martins, and V. Srikumar, Eds. Bangkok, Thailand and virtual meeting: Association for Computational Linguistics, Aug. 2024
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2023
Cited alongside, same era.
R. Rafailov, A. Sharma, E. Mitchell, C. D. Manning, S. Ermon, and C. Finn, “Direct preference optimization: Your language model is secretly a reward model,” in Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023 , A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, Eds., 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Z. Dai, V. Y. Zhao, J. Ma, Y. Luan, J. Ni, J. Lu, A. Bakalov, K. Guu, K. B. Hall, and M. Chang, “Promptagator: Few-shot dense retrieval from 8 examples,” in ICLR . OpenReview.net, 2023
2023
Cited alongside, same era.
R. Meng, Y. Liu, S. Yavuz, D. Agarwal, L. Tu, N. Yu, J. Zhang, M. Bhat, and Y. Zhou, “Augtriever: Unsupervised dense retrieval by scalable data augmentation,” 2023
2023
Cited alongside, same era.
J. Saad-Falcon, O. Khattab, K. Santhanam, R. Florian, M. Franz, S. Roukos, A. Sil, M. A. Sultan, and C. Potts, “UDAPDR: unsupervised domain adaptation via LLM prompting and distillation of rerankers,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023 , H. Bouamor, J. Pino, and K. Bali, Eds. Association for Computational Linguistics, 2023, pp. 11 265–11 279
2023
Cited alongside, same era.
Z. Peng, X. Wu, and Y. Fang, “Soft prompt tuning for augmenting dense retrieval with large language models,” 2023
2023
Cited alongside, same era.
D. S. Sachan, M. Lewis, D. Yogatama, L. Zettlemoyer, J. Pineau, and M. Zaheer, “Questions are all you need to train a dense passage retriever,” Transactions of the Association for Computational Linguistics , vol. 11, pp. 600–616, 2023
2023
Cited alongside, same era.
2024
Closest in time.
C. Meng, N. Arabzadeh, A. Askari, M. Aliannejadi, and M. de Rijke, “Ranked list truncation for large language model-based re-ranking,” in SIGIR . ACM, 2024, pp. 141–151
2024
Closest in time.
W. Shi, S. Min, M. Yasunaga, M. Seo, R. James, M. Lewis, L. Zettlemoyer, and W. Yih, “REPLUG: retrieval-augmented black-box language models,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), NAACL 2024, Mexico City, Mexico, June 16-21, 2024 , K. Duh, H. Gómez-Adorno, and S. Bethard, Eds. Association for Computational Linguistics, 2024, pp. 8371–8384
2024
Closest in time.
W. Yu, H. Zhang, X. Pan, P. Cao, K. Ma, J. Li, H. Wang, and D. Yu, “Chain-of-note: Enhancing robustness in retrieval-augmented language models,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miami, FL, USA, November 12-16, 2024 , Y. Al-Onaizan, M. Bansal, and Y. Chen, Eds. Association for Computational Linguistics, 2024, pp. 14 672–14 685
2024
Closest in time.
A. Asai, Z. Wu, Y. Wang, A. Sil, and H. Hajishirzi, “Self-rag: Learning to retrieve, generate, and critique through self-reflection,” in The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net, 2024
2024
Closest in time.
T. Vu, M. Iyyer, X. Wang, N. Constant, J. W. Wei, J. Wei, C. Tar, Y. Sung, D. Zhou, Q. V. Le, and T. Luong, “Freshllms: Refreshing large language models with search engine augmentation,” in Findings of the Association for Computational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11-16, 2024 , L. Ku, A. Martins, and V. Srikumar, Eds. Association for Computational Linguistics, 2024, pp. 13 697–13 720
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Y. Wang, R. Ren, J. Li, X. Zhao, J. Liu, and J. Wen, “REAR: A relevance-aware retrieval-augmented framework for open-domain question answering,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miami, FL, USA, November 12-16, 2024 , Y. Al-Onaizan, M. Bansal, and Y. Chen, Eds. Association for Computational Linguistics, 2024, pp. 5613–5626
2024
Closest in time.
X. V. Lin, X. Chen, M. Chen, W. Shi, M. Lomeli, R. James, P. Rodriguez, J. Kahn, G. Szilvasy, M. Lewis, L. Zettlemoyer, and W. Yih, “RA-DIT: retrieval-augmented dual instruction tuning,” in The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net, 2024
2024
Closest in time.
2024
Closest in time.
S. Xu, L. Pang, M. Yu, F. Meng, H. Shen, X. Cheng, and J. Zhou, “Unsupervised information refinement training of large language models for retrieval-augmented generation,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024 , L. Ku, A. Martins, and V. Srikumar, Eds. Association for Computational Linguistics, 2024, pp. 133–145
2024
Closest in time.
2024
Closest in time.
O. Yoran, T. Wolfson, O. Ram, and J. Berant, “Making retrieval-augmented language models robust to irrelevant context,” in The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net, 2024
2024
Closest in time.
F. Fang, Y. Bai, S. Ni, M. Yang, X. Chen, and R. Xu, “Enhancing noise robustness of retrieval-augmented language models with adaptive adversarial training,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024 , L. Ku, A. Martins, and V. Srikumar, Eds. Association for Computational Linguistics, 2024, pp. 10 028–10 039
2024
Closest in time.
J. Zhu, L. Yan, H. Shi, D. Yin, and L. Sha, “ATM: adversarial tuning multi-agent system makes a robust retrieval-augmented generator,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miami, FL, USA, November 12-16, 2024 , Y. Al-Onaizan, M. Bansal, and Y. Chen, Eds. Association for Computational Linguistics, 2024, pp. 10 902–10 919
2024
Closest in time.
Z. Feng, X. Feng, D. Zhao, M. Yang, and B. Qin, “Retrieval-generation synergy augmented large language models,” in IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2024, Seoul, Republic of Korea, April 14-19, 2024 . IEEE, 2024, pp. 11 661–11 665
2024
Closest in time.
D. Yang, J. Rao, K. Chen, X. Guo, Y. Zhang, J. Yang, and Y. Zhang, “IM-RAG: multi-round retrieval-augmented generation through learning inner monologues,” in Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024 , G. H. Yang, H. Wang, S. Han, C. Hauff, G. Zuccon, and Y. Zhang, Eds. ACM, 2024, pp. 730–740
2024
Closest in time.
Z. Shi, S. Zhang, W. Sun, S. Gao, P. Ren, Z. Chen, and Z. Ren, “Generate-then-ground in retrieval-augmented generation for multi-hop question answering,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024 , L. Ku, A. Martins, and V. Srikumar, Eds. Association for Computational Linguistics, 2024, pp. 7339–7353
2024
Closest in time.
M. Lee, S. An, and M. Kim, “Planrag: A plan-then-retrieval augmented generation for generative large language models as decision makers,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), NAACL 2024, Mexico City, Mexico, June 16-21, 2024 , K. Duh, H. Gómez-Adorno, and S. Bethard, Eds. Association for Computational Linguistics, 2024, pp. 6537–6555
2024
Closest in time.
J. Wang, M. Chen, B. Hu, D. Yang, Z. Liu, Y. Shen, P. Wei, Z. Zhang, J. Gu, J. Zhou, J. Z. Pan, W. Zhang, and H. Chen, “Learning to plan for retrieval-augmented large language models from knowledge graphs,” in Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, USA, November 12-16, 2024 , Y. Al-Onaizan, M. Bansal, and Y. Chen, Eds. Association for Computational Linguistics, 2024, pp. 7813–7835
2024
Closest in time.
M. A. Arefeen, B. Debnath, and S. Chakradhar, “Leancontext: Cost-efficient domain-specific question answering using llms,” Nat. Lang. Process. J. , vol. 7, p. 100065, 2024
2024
Closest in time.
F. Xu, W. Shi, and E. Choi, “RECOMP: improving retrieval-augmented lms with context compression and selective augmentation,” in The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net, 2024
2024
Closest in time.
X. Cheng, X. Wang, X. Zhang, T. Ge, S. Chen, F. Wei, H. Zhang, and D. Zhao, “xrag: Extreme context compression for retrieval-augmented generation with one token,” in Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024 , A. Globersons, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. M. Tomczak, and C. Zhang, Eds., 2024
2024
Closest in time.
J. Jin, Y. Zhu, Y. Zhou, and Z. Dou, “BIDER: bridging knowledge inconsistency for efficient retrieval-augmented llms via key supporting evidence,” in Findings of the Association for Computational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11-16, 2024 , L. Ku, A. Martins, and V. Srikumar, Eds. Association for Computational Linguistics, 2024, pp. 750–761
2024
Closest in time.
N. F. Liu, K. Lin, J. Hewitt, A. Paranjape, M. Bevilacqua, F. Petroni, and P. Liang, “Lost in the middle: How language models use long contexts,” Trans. Assoc. Comput. Linguistics , vol. 12, pp. 157–173, 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
S. Maekawa, H. Iso, S. Gurajada, and N. Bhutani, “Retrieval helps or hurts? A deeper dive into the efficacy of retrieval augmentation to language models,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), NAACL 2024, Mexico City, Mexico, June 16-21, 2024 , K. Duh, H. Gómez-Adorno, and S. Bethard, Eds. Association for Computational Linguistics, 2024, pp. 5506–5521
2024
Closest in time.
S. Ni, K. Bi, J. Guo, and X. Cheng, “When do llms need retrieval augmentation? mitigating llms’ overconfidence helps retrieval augmentation,” in Findings of the Association for Computational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11-16, 2024 , L. Ku, A. Martins, and V. Srikumar, Eds. Association for Computational Linguistics, 2024, pp. 11 375–11 388
2024
Closest in time.
J. Tan, Z. Dou, Y. Zhu, P. Guo, K. Fang, and J. Wen, “Small models, big insights: Leveraging slim proxy models to decide when and what to retrieve for llms,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024 , L. Ku, A. Martins, and V. Srikumar, Eds. Association for Computational Linguistics, 2024, pp. 4420–4436
2024
Closest in time.
Z. Jin, P. Cao, Y. Chen, K. Liu, X. Jiang, J. Xu, Q. Li, and J. Zhao, “Tug-of-war between knowledge: Exploring and resolving knowledge conflicts in retrieval-augmented language models,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC/COLING 2024, 20-25 May, 2024, Torino, Italy , N. Calzolari, M. Kan, V. Hoste, A. Lenci, S. Sakti, and N. Xue, Eds. ELRA and ICCL, 2024, pp. 16 867–16 878
2024
Closest in time.
S. Cho, S. Jeong, J. Seo, T. Hwang, and J. Park, “Typos that broke the rag’s back: Genetic attack on RAG pipeline by simulating documents in the wild via low-level perturbations,” in Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, USA, November 12-16, 2024 , Y. Al-Onaizan, M. Bansal, and Y. Chen, Eds. Association for Computational Linguistics, 2024, pp. 2826–2844
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
A. Louis, G. van Dijck, and G. Spanakis, “Interpretable long-form legal question answering with retrieval-augmented large language models,” in Thirty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2024, Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence, IAAI 2024, Fourteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2014, February 20-27, 2024, Vancouver, Canada , M. J. Wooldridge, J. G. Dy, and S. Natarajan, Eds. AAAI Press, 2024, pp. 22 266–22 275
2024
Closest in time.
W. Jiang, M. Zeller, R. Waleffe, T. Hoefler, and G. Alonso, “Chameleon: a heterogeneous and disaggregated accelerator system for retrieval-augmented language models,” Proc. VLDB Endow. , vol. 18, no. 1, pp. 42–52, 2024
2024
Closest in time.
I. Gur, H. Furuta, A. V. Huang, M. Safdari, Y. Matsuo, D. Eck, and A. Faust, “A real-world webagent with planning, long context understanding, and program synthesis,” in The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net, 2024
2024
Closest in time.
P. Gong, J. Li, and J. Mao, “Cosearchagent: A lightweight collaborative search agent with large language models,” in Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024 , G. H. Yang, H. Wang, S. Han, C. Hauff, G. Zuccon, and Y. Zhang, Eds. ACM, 2024, pp. 2729–2733
2024
Closest in time.
2024
Closest in time.
P. Gong, J. Li, and J. Mao, “Cosearchagent: A lightweight collaborative search agent with large language models,” in Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024 , G. H. Yang, H. Wang, S. Han, C. Hauff, G. Zuccon, and Y. Zhang, Eds. ACM, 2024, pp. 2729–2733
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
S. B. Islam, M. A. Rahman, K. S. M. T. Hossain, E. Hoque, S. Joty, and M. R. Parvez, “Open-rag: Enhanced retrieval augmented reasoning with open-source large language models,” in Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, USA, November 12-16, 2024 , Y. Al-Onaizan, M. Bansal, and Y. Chen, Eds. Association for Computational Linguistics, 2024, pp. 14 231–14 244
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
G. Mialon, C. Fourrier, T. Wolf, Y. LeCun, and T. Scialom, “GAIA: a benchmark for general AI assistants,” in The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net, 2024
2024
Closest in time.
O. Yoran, S. J. Amouyal, C. Malaviya, B. Bogin, O. Press, and J. Berant, “Assistantbench: Can web agents solve realistic and time-consuming tasks?” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miami, FL, USA, November 12-16, 2024 , Y. Al-Onaizan, M. Bansal, and Y. Chen, Eds. Association for Computational Linguistics, 2024, pp. 8938–8968
2024
Closest in time.
2024
Closest in time.
N. Muennighoff, Q. Liu, A. R. Zebaze, Q. Zheng, B. Hui, T. Y. Zhuo, S. Singh, X. Tang, L. von Werra, and S. Longpre, “Octopack: Instruction tuning code large language models,” in The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net, 2024
2024
Closest in time.
Q. Huang, J. Vora, P. Liang, and J. Leskovec, “Mlagentbench: Evaluating language agents on machine learning experimentation,” in Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024 . OpenReview.net, 2024
2024
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2024
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S. Zhou, F. F. Xu, H. Zhu, X. Zhou, R. Lo, A. Sridhar, X. Cheng, T. Ou, Y. Bisk, D. Fried, U. Alon, and G. Neubig, “Webarena: A realistic web environment for building autonomous agents,” in The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net, 2024
2024
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J. Zhang, R. Xie, Y. Hou, X. Zhao, L. Lin, and J. Wen, “Recommendation as instruction following: A large language model empowered recommendation approach,” ACM Trans. Inf. Syst. , vol. 43, no. 5, pp. 114:1–114:37, 2025
2025
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I. Baek, J. Lee, J. Yang, and H. Lee, “Crafting the path: Robust query rewriting for information retrieval,” IEEE Access , vol. 13, pp. 24 171–24 180, 2025
2025
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2025
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I. Baek, J. Lee, J. Yang, and H. Lee, “Crafting the path: Robust query rewriting for information retrieval,” IEEE Access , vol. 13, pp. 24 171–24 180, 2025
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C. Lee, R. Roy, M. Xu, J. Raiman, M. Shoeybi, B. Catanzaro, and W. Ping, “Nv-embed: Improved techniques for training llms as generalist embedding models,” in The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025 . OpenReview.net, 2025
2025
Closest in time.
C. Zhang, S. Hofstätter, P. Lewis, R. Tang, and J. Lin, “Rank-without-gpt: Building gpt-independent listwise rerankers on open-source large language models,” in Advances in Information Retrieval - 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, April 6-10, 2025, Proceedings, Part II , ser. Lecture Notes in Computer Science, C. Hauff, C. Macdonald, D. Jannach, G. Kazai, F. M. Nardini, F. Pinelli, F. Silvestri, and N. Tonellotto, Eds., vol. 15573. Springer, 2025, pp. 233–247
2025
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2025
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C. Jin, H. Peng, S. Zhao, Z. Wang, W. Xu, L. Han, J. Zhao, K. Zhong, S. Rajasekaran, and D. N. Metaxas, “APEER : Automatic prompt engineering enhances large language model reranking,” in WWW (Companion Volume) . ACM, 2025, pp. 2494–2502
2025
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H. Su, H. Yen, M. Xia, W. Shi, N. Muennighoff, H. Wang, H. Liu, Q. Shi, Z. S. Siegel, M. Tang, R. Sun, J. Yoon, S. Ö. Arik, D. Chen, and T. Yu, “BRIGHT: A realistic and challenging benchmark for reasoning-intensive retrieval,” in ICLR . OpenReview.net, 2025
2025
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S. Chen, B. J. Gutierrez, and Y. Su, “Attention in large language models yields efficient zero-shot re-rankers,” in ICLR . OpenReview.net, 2025
2025
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S. Wang, X. Yu, M. Wang, W. Chen, Y. Zhu, and Z. Dou, “Richrag: Crafting rich responses for multi-faceted queries in retrieval-augmented generation,” in Proceedings of the 31st International Conference on Computational Linguistics, COLING 2025, Abu Dhabi, UAE, January 19-24, 2025 , O. Rambow, L. Wanner, M. Apidianaki, H. Al-Khalifa, B. D. Eugenio, and S. Schockaert, Eds. Association for Computational Linguistics, 2025, pp. 11 317–11 333
2025
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2025
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Y. Zhu, Z. Huang, Z. Dou, and J. Wen, “One token can help! learning scalable and pluggable virtual tokens for retrieval-augmented large language models,” in AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25 - March 4, 2025, Philadelphia, PA, USA , T. Walsh, J. Shah, and Z. Kolter, Eds. AAAI Press, 2025, pp. 26 166–26 174
2025
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R. Ren, Y. Wang, Y. Qu, W. X. Zhao, J. Liu, H. Wu, J. Wen, and H. Wang, “Investigating the factual knowledge boundary of large language models with retrieval augmentation,” in Proceedings of the 31st International Conference on Computational Linguistics, COLING 2025, Abu Dhabi, UAE, January 19-24, 2025 , O. Rambow, L. Wanner, M. Apidianaki, H. Al-Khalifa, B. D. Eugenio, and S. Schockaert, Eds. Association for Computational Linguistics, 2025, pp. 3697–3715
2025
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J. Jin, Y. Zhu, Z. Dou, G. Dong, X. Yang, C. Zhang, T. Zhao, Z. Yang, and J. Wen, “Flashrag: A modular toolkit for efficient retrieval-augmented generation research,” in Companion Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025 - 2 May 2025 , G. Long, M. Blumestein, Y. Chang, L. Lewin-Eytan, Z. H. Huang, and E. Yom-Tov, Eds. ACM, 2025, pp. 737–740
2025
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X. Shi, J. Liu, Y. Liu, Q. Cheng, and W. Lu, “Know where to go: Make LLM a relevant, responsible, and trustworthy searchers,” Decis. Support Syst. , vol. 188, p. 114354, 2025
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Y. Deng, G. Wang, Z. Ying, X. Wu, J. Lin, W. Xiong, Y. Dai, S. Yang, Z. Zhang, Q. Wang, Y. Qin, Y. Wang, Q. Zha, S. Dai, and C. Meng, “Atom-searcher: Enhancing agentic deep research via fine-grained atomic thought reward,” 2025
2025
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L. Mei, Z. Yang, and C. Chen, “Ai-searchplanner: Modular agentic search via pareto-optimal multi-objective reinforcement learning,” 2025
2025
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Z. Chen, K. Liu, Q. Wang, J. Liu, W. Zhang, K. Chen, and F. Zhao, “Mindsearch: Mimicking human minds elicits deep AI searcher,” in The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025 . OpenReview.net, 2025
2025
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K. Wan, H. Mu, R. Hao, H. Luo, T. Gu, and X. Chen, “A cognitive writing perspective for constrained long-form text generation,” in Findings of the Association for Computational Linguistics, ACL 2025, Vienna, Austria, July 27 - August 1, 2025 , W. Che, J. Nabende, E. Shutova, and M. T. Pilehvar, Eds. Association for Computational Linguistics, 2025, pp. 9832–9844
2025
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Z. Chen, M. Li, Y. Huang, Y. Du, M. Fang, and T. Zhou, “ATLAS: agent tuning via learning critical steps,” in Findings of the Association for Computational Linguistics, ACL 2025, Vienna, Austria, July 27 - August 1, 2025 , W. Che, J. Nabende, E. Shutova, and M. T. Pilehvar, Eds. Association for Computational Linguistics, 2025, pp. 25 334–25 349
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J. S. Chan, N. Chowdhury, O. Jaffe, J. Aung, D. Sherburn, E. Mays, G. Starace, K. Liu, L. Maksin, T. Patwardhan, A. Madry, and L. Weng, “Mle-bench: Evaluating machine learning agents on machine learning engineering,” in The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025 . OpenReview.net, 2025
2025
Closest in time.
J. Chen, D. Yuen, B. Xie, Y. Yang, G. Chen, Z. Wu, L. Yixing, X. Zhou, W. Liu, S. Wang, K. Zhou, R. Shao, L. Nie, Y. Wang, J. Hao, J. Wang, and K. Shao, “Spa-bench: a comprehensive benchmark for smartphone agent evaluation,” in The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025 . OpenReview.net, 2025
2025
Closest in time.
J. Wu, W. Yin, Y. Jiang, Z. Wang, Z. Xi, R. Fang, L. Zhang, Y. He, D. Zhou, P. Xie, and F. Huang, “Webwalker: Benchmarking llms in web traversal,” in Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2025, Vienna, Austria, July 27 - August 1, 2025 , W. Che, J. Nabende, E. Shutova, and M. T. Pilehvar, Eds. Association for Computational Linguistics, 2025, pp. 10 290–10 305
2025
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N. Muennighoff, N. Tazi, L. Magne, and N. Reimers, “MTEB: massive text embedding benchmark,” in Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023, Dubrovnik, Croatia, May 2-6, 2023 , A. Vlachos and I. Augenstein, Eds. Association for Computational Linguistics, 2023, pp. 2006–2029
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