{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:07:18Z","timestamp":1780675638175,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","funder":[{"name":"National Natural Science Foundation of China under Grant","award":["62306267"],"award-info":[{"award-number":["62306267"]}]},{"name":"Zhejiang Province &#x5c;&quot;Lingyan&#x5c;&quot; Key R&D Project of China","award":["No.2026C02A2002"],"award-info":[{"award-number":["No.2026C02A2002"]}]},{"name":"Zhejiang Province &ldquo;Jianbing&rdquo; Key R&D Project of China","award":["No.2025C01010"],"award-info":[{"award-number":["No.2025C01010"]}]},{"name":"Zhejiang Province &ldquo;Jianbing&rdquo; Key R&D Project of China","award":["No.2024C01034"],"award-info":[{"award-number":["No.2024C01034"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,4,13]]},"DOI":"10.1145\/3774904.3792289","type":"proceedings-article","created":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T21:54:34Z","timestamp":1775771674000},"page":"2114-2125","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Conflict-Aware RAG: Multi-Stage Learning with Conflict Signals for Robust Retrieval-Augmented Generation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0722-3042","authenticated-orcid":false,"given":"Haiyan","family":"Wu","sequence":"first","affiliation":[{"name":"Zhejiang University of Finance and Economics, Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5001-627X","authenticated-orcid":false,"given":"Chenchen","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University of Finance and Economics, Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1401-7767","authenticated-orcid":false,"given":"Chaoqun","family":"Sun","sequence":"additional","affiliation":[{"name":"Zhejiang University of Finance and Economics, Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4864-1009","authenticated-orcid":false,"given":"Chengxiong","family":"Lu","sequence":"additional","affiliation":[{"name":"Zhejiang University of Finance and Economics, Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7857-175X","authenticated-orcid":false,"given":"Zhiqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang University of Finance and Economics, Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6853-4902","authenticated-orcid":false,"given":"Yanhong","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University of Finance and Economics, Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,12]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al.","author":"Achiam Josh","year":"2023","unstructured":"Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)."},{"key":"e_1_3_2_1_2_1","volume-title":"The Twelfth International Conference on Learning Representations, ICLR 2024","author":"Asai Akari","year":"2024","unstructured":"Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2024. 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. https:\/\/openreview.net\/forum?id=hSyW5go0v8"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D13-1160"},{"key":"e_1_3_2_1_4_1","volume-title":"Parameters vs. context: Fine-grained control of knowledge reliance in language models. arXiv preprint arXiv:2503.15888","author":"Bi Baolong","year":"2025","unstructured":"Baolong Bi, Shenghua Liu, Yiwei Wang, Yilong Xu, Junfeng Fang, Lingrui Mei, and Xueqi Cheng. 2025. Parameters vs. context: Fine-grained control of knowledge reliance in language models. arXiv preprint arXiv:2503.15888 (2025)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.146"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.emnlp-main.1371"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714717"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671470"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.naacl-main.194"},{"key":"e_1_3_2_1_10_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning (ICML'20)","author":"Guu Kelvin","year":"2020","unstructured":"Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020. REALM: retrieval-augmented language model pre-training. In Proceedings of the 37th International Conference on Machine Learning (ICML'20). JMLR.org, Vienna, Austria (Online), Article 368, 10 pages."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.580"},{"key":"e_1_3_2_1_12_1","volume-title":"Saku Sugawara, and Akiko Aizawa.","author":"Ho Xanh","year":"2020","unstructured":"Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2020. Constructing a multi-hop qa dataset for comprehensive evaluation of reasoning steps. arXiv preprint arXiv:2011.01060 (2020)."},{"key":"e_1_3_2_1_13_1","volume-title":"The Thirteenth International Conference on Learning Representations, ICLR 2025","author":"Huang Yukun","year":"2025","unstructured":"Yukun Huang, Sanxing Chen, Hongyi Cai, and Bhuwan Dhingra. 2025. To Trust or Not to Trust? Enhancing Large Language Models' Situated Faithfulness to External Contexts. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24--28, 2025. OpenReview.net, Singapore, 1--30. https:\/\/openreview.net\/forum?id=K2jOacHUlO"},{"key":"e_1_3_2_1_14_1","article-title":"Atlas: Few-shot Learning with Retrieval Augmented Language Models","volume":"24","author":"Izacard Gautier","year":"2023","unstructured":"Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2023. Atlas: Few-shot Learning with Retrieval Augmented Language Models. J. Mach. Learn. Res. 24 (2023), 251:1--251:43. https:\/\/jmlr.org\/papers\/v24\/23-0037.html","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.acl-long.527"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.63317\/4fisde58hr4n"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1147"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.5555\/3618408.3619049"},{"key":"e_1_3_2_1_19_1","volume-title":"Scaling laws for neural language models. arXiv preprint arXiv:2001.08361","author":"Kaplan Jared","year":"2020","unstructured":"Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361 (2020)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00276"},{"key":"e_1_3_2_1_22_1","unstructured":"Patrick Lewis Ethan Perez Aleksandra Piktus Fabio Petroni Vladimir Karpukhin Naman Goyal Heinrich K\u00fcttler Mike Lewis Wen-tau Yih Tim Rockt\u00e4schel et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in neural information processing systems 33 (2020) 9459--9474."},{"key":"e_1_3_2_1_23_1","volume-title":"RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards. In The Thirteenth International Conference on Learning Representations, ICLR 2025","author":"Li Xinze","year":"2025","unstructured":"Xinze Li, Sen Mei, Zhenghao Liu, Yukun Yan, Shuo Wang, Shi Yu, Zheni Zeng, Hao Chen, Ge Yu, Zhiyuan Liu, Maosong Sun, and Chenyan Xiong. 2025. RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24--28, 2025. OpenReview.net. https:\/\/openreview.net\/forum?id=Pnktu2PBXD"},{"key":"e_1_3_2_1_24_1","first-page":"115588","article-title":"Flame: Factuality-aware alignment for large language models","volume":"37","author":"Lin Sheng-Chieh","year":"2024","unstructured":"Sheng-Chieh Lin, Luyu Gao, Barlas Oguz, Wenhan Xiong, Jimmy Lin, Wen-tau Yih, and Xilun Chen. 2024. Flame: Factuality-aware alignment for large language models. Advances in Neural Information Processing Systems 37 (2024), 115588--115614.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_25_1","volume-title":"RA-DIT: Retrieval-Augmented Dual Instruction Tuning. In The Twelfth International Conference on Learning Representations, ICLR 2024","author":"Lin Xi Victoria","year":"2024","unstructured":"Xi Victoria Lin, Xilun Chen, Mingda Chen, Weijia Shi, Maria Lomeli, Richard James, Pedro Rodriguez, Jacob Kahn, Gergely Szilvasy, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih. 2024. 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. https:\/\/openreview.net\/forum?id=22OTbutug9"},{"key":"e_1_3_2_1_26_1","volume-title":"The Thirteenth International Conference on Learning Representations, ICLR 2025","author":"Ming Yifei","year":"2025","unstructured":"Yifei Ming, Senthil Purushwalkam, Shrey Pandit, Zixuan Ke, Xuan-Phi Nguyen, Caiming Xiong, and Shafiq Joty. 2025. FaithEval: Can Your Language Model Stay Faithful to Context, Even If ''The Moon is Made of Marshmallows''. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24--28, 2025. OpenReview.net. https:\/\/openreview.net\/forum?id=UeVx6L59fg"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3744746"},{"key":"e_1_3_2_1_28_1","unstructured":"Baolin Peng Michel Galley Pengcheng He Hao Cheng Yujia Xie Yu Hu Qiuyuan Huang Lars Liden Zhou Yu Weizhu Chen et al. 2023. Check your facts and try again: Improving large language models with external knowledge and automated feedback. arXiv preprint arXiv:2302.12813 (2023)."},{"key":"e_1_3_2_1_29_1","volume-title":"Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023","author":"Rafailov Rafael","year":"2023","unstructured":"Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D. Manning, Stefano Ermon, and Chelsea Finn. 2023. 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, Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine (Eds.). http:\/\/papers.nips.cc\/paper_files\/paper\/2023\/hash\/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html"},{"key":"e_1_3_2_1_30_1","volume-title":"100,000 questions for machine comprehension of text. arXiv preprint arXiv:1606.05250","author":"Rajpurkar Pranav","year":"2016","unstructured":"Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. Squad: 100,000 questions for machine comprehension of text. arXiv preprint arXiv:1606.05250 (2016)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00605"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.naacl-short.69"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.naacl-long.463"},{"key":"e_1_3_2_1_34_1","volume-title":"The Thirteenth International Conference on Learning Representations, ICLR 2025","author":"Song Maojia","year":"2025","unstructured":"Maojia Song, Shang Hong Sim, Rishabh Bhardwaj, Hai Leong Chieu, Navonil Majumder, and Soujanya Poria. 2025. Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24--28, 2025. OpenReview.net. https:\/\/openreview.net\/forum?id=Iyrtb9EJBp"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.acl-long.561"},{"key":"e_1_3_2_1_36_1","volume-title":"The Thirteenth International Conference on Learning Representations, ICLR 2025","author":"Sun Zhongxiang","year":"2025","unstructured":"Zhongxiang Sun, Xiaoxue Zang, Kai Zheng, Jun Xu, Xiao Zhang, Weijie Yu, Yang Song, and Han Li. 2025. ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24--28, 2025. OpenReview.net. https:\/\/openreview.net\/forum?id=ztzZDzgfrh"},{"key":"e_1_3_2_1_37_1","volume-title":"Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971","author":"Touvron Hugo","year":"2023","unstructured":"Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth\u00e9e Lacroix, Baptiste Rozi\u00e8re, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)."},{"key":"e_1_3_2_1_38_1","volume-title":"Text embeddings by weakly-supervised contrastive pre-training. arXiv preprint arXiv:2212.03533","author":"Wang Liang","year":"2022","unstructured":"Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022. Text embeddings by weakly-supervised contrastive pre-training. arXiv preprint arXiv:2212.03533 (2022)."},{"key":"e_1_3_2_1_39_1","volume-title":"The Thirteenth International Conference on Learning Representations, ICLR 2025","author":"Wei Zhepei","year":"2025","unstructured":"Zhepei Wei, Wei-Lin Chen, and Yu Meng. 2025. InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24--28, 2025. OpenReview.net. https:\/\/openreview.net\/forum?id=P1qhkp8gQT"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.naacl-long.459"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.acl-long.629"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657878"},{"key":"e_1_3_2_1_43_1","volume-title":"The Twelfth International Conference on Learning Representations, ICLR 2024","author":"Xie Jian","year":"2024","unstructured":"Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, and Yu Su. 2024. Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7--11, 2024. OpenReview.net, Vienna, Austria, 1--24. https:\/\/openreview.net\/forum?id=auKAUJZMO6"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.486"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","unstructured":"Yongxin Xu Ruizhe Zhang Xinke Jiang Yujie Feng Yuzhen Xiao Xinyu Ma Runchuan Zhu Xu Chu Junfeng Zhao and Yasha Wang. 2025. Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) Wanxiang Che Joyce Nabende Ekaterina Shutova and Mohammad Taher Pilehvar (Eds.). Association for Computational Linguistics Vienna Austria 11643--11662. doi:10.18653\/v1\/2025.acl-long.571","DOI":"10.18653\/v1\/2025.acl-long.571"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.acl-long.261"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.232"},{"key":"e_1_3_2_1_48_1","volume-title":"Augmentation-adapted retriever improves generalization of language models as generic plug-in. arXiv preprint arXiv:2305.17331","author":"Yu Zichun","year":"2023","unstructured":"Zichun Yu, Chenyan Xiong, Shi Yu, and Zhiyuan Liu. 2023. Augmentation-adapted retriever improves generalization of language models as generic plug-in. arXiv preprint arXiv:2305.17331 (2023)."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.acl-long.1062"},{"key":"e_1_3_2_1_50_1","volume-title":"Raft: Adapting language model to domain specific rag. arXiv preprint arXiv:2403.10131","author":"Zhang Tianjun","year":"2024","unstructured":"Tianjun Zhang, Shishir G Patil, Naman Jain, Sheng Shen, Matei Zaharia, Ion Stoica, and Joseph E Gonzalez. 2024. Raft: Adapting language model to domain specific rag. arXiv preprint arXiv:2403.10131 (2024)."},{"key":"e_1_3_2_1_51_1","unstructured":"Yue Zhang Yafu Li Leyang Cui Deng Cai Lemao Liu Tingchen Fu Xinting Huang Enbo Zhao Yu Zhang Yulong Chen et al. 2025. Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models. Computational Linguistics (2025) 1--46."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.968"}],"event":{"name":"WWW '26: The ACM Web Conference 2026","location":"Dubai United Arab Emirates","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2026"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3774904.3792289","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T15:56:35Z","timestamp":1780674995000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774904.3792289"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,12]]},"references-count":52,"alternative-id":["10.1145\/3774904.3792289","10.1145\/3774904"],"URL":"https:\/\/doi.org\/10.1145\/3774904.3792289","relation":{},"subject":[],"published":{"date-parts":[[2026,4,12]]},"assertion":[{"value":"2026-04-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}