{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:29:58Z","timestamp":1755221398031,"version":"3.43.0"},"reference-count":68,"publisher":"Association for Computing Machinery (ACM)","issue":"5","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>Conversational retrieval leverages multi-turn conversations to meet users\u2019 information needs, and accurately understanding the new intent has become a significant challenge in this field. Recently, the language comprehension and reasoning capabilities of large language models (LLMs) offer a viable solution to these challenges. In this article, we propose a new Dual-Query Generation and Joint-Encoding method by utilizing LLM for Conversational Information Seeking, abbreviated as DQ-CIS. Specifically, we propose a dual-query generation approach that leverages both open source and closed source LLMs to generate two complementary queries: a full-rewrite query that preserves the context semantics of the conversation and a condensed-rewrite query that emphasizes the core intent of the current query. Additionally, to better express the semantic information of the query, we propose a dual-query joint-encoding method, which enhances the thematic expression of query vectors by treating the dual-query as semantic complementary. A query coverage fine-tuned semantic matching method is also introduced to improve result relevance and ranking by fine-tuning the original retrieval scores by ColBERT. We conducted a number of experiments on seven publicly available conversational retrieval datasets. The results show that compared with other models, DQ-CIS has strong competitiveness in both retrieval efficiency and retrieval results.<\/jats:p>","DOI":"10.1145\/3742423","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T11:15:03Z","timestamp":1750850103000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Utilizing Large Language Model for Conversational Information Seeking via Dual-Query Generation and Joint-Encoding"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0804-4281","authenticated-orcid":false,"given":"Junmei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science, Hangzhou Dianzi University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4042-066X","authenticated-orcid":false,"given":"Fengjing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6989-1073","authenticated-orcid":false,"given":"Xiadan","family":"Chen","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7565-9685","authenticated-orcid":false,"given":"Puyu","family":"He","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0887-0331","authenticated-orcid":false,"given":"Ellen Anne","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Western University, London, Ontario, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1292-1491","authenticated-orcid":false,"given":"Jimmy Xiangji","family":"Huang","sequence":"additional","affiliation":[{"name":"Information Retrieval and Knowledge Management Research Lab, York University, Toronto, Ontario, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,8]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Zahra Abbasiantaeb and Mohammad Aliannejadi. 2024. Generate then retrieve: Conversational response retrieval using LLMs as answer and query generators. arXiv:2403.19302. Retrieved from https:\/\/arxiv.org\/abs\/2403.19302"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00471"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657860"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.44"},{"key":"e_1_3_1_6_2","unstructured":"Payal Bajaj Daniel Campos Nick Craswell Li Deng Jianfeng Gao Xiaodong Liu Rangan Majumder Andrew McNamara Bhaskar Mitra Tri Nguyen et al. 2018. MS MARCO: A human generated machine reading comprehension dataset. arXiv:1611.09268. Retrieved from https:\/\/arxiv.org\/abs\/1611.09268"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-industry.36"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.159"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Jeffrey Dalton Chenyan Xiong and Jamie Callan. 2020. CAsT 2020: The conversational assistance track overview. In Proceedings of the Text Retrieval Conference. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:214735659","DOI":"10.6028\/NIST.SP.1266.cast-overview"},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Jeffrey Dalton Chenyan Xiong and Jamie Callan. 2020. TREC CAsT 2019: The conversational assistance track overview. arXiv:2003.13624 (2020). Retrieved from https:\/\/arxiv.org\/abs\/2003.13624","DOI":"10.6028\/NIST.SP.1266.cast-overview"},{"key":"e_1_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Jeffrey Dalton Chenyan Xiong and Jamie Callan. 2021. TREC CAsT 2021: The conversational assistance track overview. In Proceedings of the Text Retrieval Conference. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:261241621","DOI":"10.6028\/NIST.SP.500-335.cast-overview"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1423"},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","unstructured":"Laura Dietz Manisha Verma Filip Radlinski and Nick Craswell. 2018. TREC complex answer retrieval overview. In Proceedings of the Text Retrieval Conference. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:4987800","DOI":"10.6028\/NIST.SP.500-331.car-overview"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-88711-6_3"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.IPM.2012.08.002"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.naacl-long.449"},{"key":"e_1_3_1_17_2","unstructured":"Albert Qiaochu Jiang Alexandre Sablayrolles Arthur Mensch Chris Bamford Devendra Singh Chaplot Diego de Las Casas Florian Bressand Gianna Lengyel Guillaume Lample and Lucile Saulnier et al. 2023. Mistral 7B. arXiv:2310.06825. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:263830494"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.443"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.6028\/NIST.SP.500-338.cast-CFDA_CLIP"},{"key":"e_1_3_1_20_2","doi-asserted-by":"crossref","unstructured":"Kimiya Keyvan and Jimmy Xiangji Huang. 2023. How to approach ambiguous queries in conversational search: A\u00a0survey of techniques approaches tools and challenges. ACM Computing Surveys 55 6 Article 129 (Dec. 2023) 40\u00a0pages. Retrieved from https:\/\/doi.org\/10.1145\/3534965","DOI":"10.1145\/3534965"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401075"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531769"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3632622"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.77"},{"key":"e_1_3_1_25_2","unstructured":"Sheng-Chieh Lin Jheng-Hong Yang Rodrigo Nogueira Ming-Feng Tsai Chuan-Ju Wang and Jimmy Lin. 2020. Conversational question reformulation via sequence-to-sequence architectures and pretrained language models. arXiv:2004.01909. Retrieved from https:\/\/arxiv.org\/abs\/2004.01909"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3446426"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.71"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.256"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.86"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531961"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.135"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.274"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599411"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679534"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-acl.792"},{"key":"e_1_3_1_36_2","unstructured":"Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya Florencia Leoni Aleman Diogo Almeida Janko Altenschmidt Sam Altman Shyamal Anadkat et al. 2024. GPT-4 technical report. arXiv:2303.08774. Retrieved from https:\/\/arxiv.org\/abs\/2303.08774"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591683"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.200"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.534"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401110"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3432726"},{"key":"e_1_3_1_42_2","doi-asserted-by":"crossref","unstructured":"Stephen E. Robertson Steve Walker Micheline Hancock-Beaulieu Mike Gatford and A. Payne. 1995. Okapi at TREC-4. In Proceedings of the Text Retrieval Conference. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:14137141","DOI":"10.6028\/NIST.SP.500-236.interactive-city"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-emnlp.622"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.naacl-main.272"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.923"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3439869"},{"key":"e_1_3_1_47_2","unstructured":"Hugo Touvron Louis Martin Kevin R. Stone Peter Albert Amjad Almahairi Yasmine Babaei Nikolay Bashlykov Soumya Batra Prajjwal Bhargava Shruti Bhosale et al. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv:2307.09288. Retrieved from https:\/\/arxiv.org\/abs\/2307.09288"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-88714-7_26"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441748"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210065"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401130"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-023-01192-3"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102342"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.585"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-88720-8_37"},{"issue":"24","key":"e_1_3_1_56_2","doi-asserted-by":"crossref","first-page":"25434","DOI":"10.1609\/aaai.v39i24.34732","article-title":"MaFeRw: Query rewriting with multi-aspect feedbacks for retrieval-augmented large language models","volume":"39","author":"Wang Yujing","year":"2025","unstructured":"Yujing Wang, Hainan Zhang, Liang Pang, Binghui Guo, Hongwei Zheng, and Zhiming Zheng. 2025. MaFeRw: Query rewriting with multi-aspect feedbacks for retrieval-augmented large language models. In Proceedings of the AAAI Conference on Artificial Intelligence 39, 24 (2025), 25434\u201325442.","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.679"},{"key":"e_1_3_1_58_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Xiong Lee","year":"2021","unstructured":"Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N. Bennett, Junaid Ahmed, and Arnold Overwijk. 2021. Approximate nearest neighbor negative contrastive learning for dense text retrieval. In Proceedings of the International Conference on Learning Representations. Retrieved from https:\/\/openreview.net\/forum?id=zeFrfgyZln"},{"key":"e_1_3_1_59_2","doi-asserted-by":"crossref","unstructured":"Dayu Yang Yue Zhang and H. Fang. 2023. Mixed-initiative query rewriting in conversational passage retrieval. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:259951092","DOI":"10.6028\/NIST.SP.500-338.cast-udel_fang"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.398"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","unstructured":"Zheng Ye Jimmy Xiangji Huang and Hongfei Lin. 2011. Finding a good query-related topic for boosting pseudo-relevance feedback. Journal of the Association for Information Science and Technology 62 4 (2011) 748\u2013760. DOI: 10.1002\/ASI.21501","DOI":"10.1002\/ASI.21501"},{"key":"e_1_3_1_62_2","doi-asserted-by":"crossref","first-page":"5899","DOI":"10.18653\/v1\/2025.findings-naacl.328","volume-title":"Proceedings of the Findings of the Association for Computational Linguistics (NAACL \u201925)","author":"Yoon Chanwoong","year":"2025","unstructured":"Chanwoong Yoon, Gangwoo Kim, Byeongguk Jeon, Sungdong Kim, Yohan Jo, and Jaewoo Kang. 2025. Ask optimal questions: Aligning large language models with retriever\u2018s preference in conversation. In Proceedings of the Findings of the Association for Computational Linguistics (NAACL \u201925). Luis Chiruzzo, Alan Ritter, and Lu Wang (Eds.), Association for Computational Linguistics, 5899\u20135921. Retrieved from https:\/\/aclanthology.org\/2025.findings-naacl.328\/"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401323"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462856"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.IPM.2022.102933"},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1145\/2009916.2009941"},{"key":"e_1_3_1_67_2","doi-asserted-by":"publisher","DOI":"10.1145\/2590988"},{"key":"e_1_3_1_68_2","first-page":"1","article-title":"Learning to ask: Conversational product search via representation learning","volume":"41","author":"Zou Jie","year":"2022","unstructured":"Jie Zou, J. Huang, Z. Ren, and E. Kanoulas. 2022. Learning to ask: Conversational product search via representation learning. ACM Transactions on Information Systems 41 (2022), 1\u201327.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1145\/3677376"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3742423","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T17:55:16Z","timestamp":1754675716000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3742423"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,8]]},"references-count":68,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,9,30]]}},"alternative-id":["10.1145\/3742423"],"URL":"https:\/\/doi.org\/10.1145\/3742423","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"type":"print","value":"1046-8188"},{"type":"electronic","value":"1558-2868"}],"subject":[],"published":{"date-parts":[[2025,8,8]]},"assertion":[{"value":"2024-09-06","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-19","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}