{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:27:17Z","timestamp":1778081237162,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":53,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3672065","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:54:55Z","timestamp":1724561695000},"page":"199-210","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1503-6519","authenticated-orcid":false,"given":"Tianchi","family":"Cai","sequence":"first","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4833-1375","authenticated-orcid":false,"given":"Zhiwen","family":"Tan","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4580-1683","authenticated-orcid":false,"given":"Xierui","family":"Song","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6357-6726","authenticated-orcid":false,"given":"Tao","family":"Sun","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1083-2834","authenticated-orcid":false,"given":"Jiyan","family":"Jiang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4472-2930","authenticated-orcid":false,"given":"Yunqi","family":"Xu","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6453-7462","authenticated-orcid":false,"given":"Yinger","family":"Zhang","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7596-4945","authenticated-orcid":false,"given":"Jinjie","family":"Gu","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_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_2_2_1","volume-title":"Query Refinement Prompts for Closed-Book Long-Form Question Answering. arXiv preprint arXiv:2210.17525","author":"Amplayo Reinald Kim","year":"2022","unstructured":"Reinald Kim Amplayo, Kellie Webster, Michael Collins, Dipanjan Das, and Shashi Narayan. 2022. Query Refinement Prompts for Closed-Book Long-Form Question Answering. arXiv preprint arXiv:2210.17525 (2022)."},{"key":"e_1_3_2_2_3_1","unstructured":"Amanda Askell Yuntao Bai Anna Chen Dawn Drain Deep Ganguli Tom Henighan Andy Jones Nicholas Joseph Ben Mann Nova DasSarma et al. 2021. A general language assistant as a laboratory for alignment. arXiv preprint arXiv:2112.00861 (2021)."},{"key":"e_1_3_2_2_4_1","unstructured":"Yuntao Bai Andy Jones Kamal Ndousse Amanda Askell Anna Chen Nova DasSarma Dawn Drain Stanislav Fort Deep Ganguli Tom Henighan et al. 2022. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862 (2022)."},{"key":"e_1_3_2_2_5_1","unstructured":"Yushi Bai Jiahao Ying Yixin Cao Xin Lv Yuze He Xiaozhi Wang Jifan Yu Kaisheng Zeng Yijia Xiao Haozhe Lyu Jiayin Zhang Juanzi Li and Lei Hou. 2023. Benchmarking Foundation Models with Language-Model-as-an-Examiner. arxiv: 2306.04181 [cs.CL]"},{"key":"e_1_3_2_2_6_1","volume-title":"International conference on machine learning. PMLR, 2206--2240","author":"Borgeaud Sebastian","year":"2022","unstructured":"Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al. 2022. Improving language models by retrieving from trillions of tokens. In International conference on machine learning. PMLR, 2206--2240."},{"key":"e_1_3_2_2_7_1","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems Vol. 33 (2020) 1877--1901."},{"key":"e_1_3_2_2_8_1","volume-title":"FELM: Benchmarking Factuality Evaluation of Large Language Models. arxiv: 2310.00741 [cs.CL]","author":"Chen Shiqi","year":"2023","unstructured":"Shiqi Chen, Yiran Zhao, Jinghan Zhang, I-Chun Chern, Siyang Gao, Pengfei Liu, and Junxian He. 2023. FELM: Benchmarking Factuality Evaluation of Large Language Models. arxiv: 2310.00741 [cs.CL]"},{"key":"e_1_3_2_2_9_1","unstructured":"I Chern Steffi Chern Shiqi Chen Weizhe Yuan Kehua Feng Chunting Zhou Junxian He Graham Neubig Pengfei Liu et al. 2023. FacTool: Factuality Detection in Generative AI--A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios. arXiv preprint arXiv:2307.13528 (2023)."},{"key":"e_1_3_2_2_10_1","volume-title":"Deep reinforcement learning from human preferences. Advances in neural information processing systems","author":"Christiano Paul F","year":"2017","unstructured":"Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017. Deep reinforcement learning from human preferences. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.26"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1346"},{"key":"e_1_3_2_2_13_1","volume-title":"GPTScore: Evaluate as You Desire. arXiv preprint arXiv:2302.04166","author":"Fu Jinlan","year":"2023","unstructured":"Jinlan Fu, See-Kiong Ng, Zhengbao Jiang, and Pengfei Liu. 2023. GPTScore: Evaluate as You Desire. arXiv preprint arXiv:2302.04166 (2023)."},{"key":"e_1_3_2_2_14_1","volume-title":"Enabling Large Language Models to Generate Text with Citations. arXiv preprint arXiv:2305.14627","author":"Gao Tianyu","year":"2023","unstructured":"Tianyu Gao, Howard Yen, Jiatong Yu, and Danqi Chen. 2023. Enabling Large Language Models to Generate Text with Citations. arXiv preprint arXiv:2305.14627 (2023)."},{"key":"e_1_3_2_2_15_1","unstructured":"Biyang Guo Xin Zhang Ziyuan Wang Minqi Jiang Jinran Nie Yuxuan Ding Jianwei Yue and Yupeng Wu. 2023. How Close is ChatGPT to Human Experts? Comparison Corpus Evaluation and Detection. arxiv: 2301.07597 [cs.CL]"},{"key":"e_1_3_2_2_16_1","volume-title":"International conference on machine learning. PMLR, 3929--3938","author":"Guu Kelvin","year":"2020","unstructured":"Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020. Retrieval augmented language model pre-training. In International conference on machine learning. PMLR, 3929--3938."},{"key":"e_1_3_2_2_17_1","unstructured":"Xiangkun Hu Dongyu Ru Qipeng Guo Lin Qiu and Zheng Zhang. 2023. RefChecker for Fine-grained Hallucination Detection. (2023). https:\/\/github.com\/amazon-science\/RefChecker"},{"key":"e_1_3_2_2_18_1","volume-title":"Unsupervised dense information retrieval with contrastive learning. arXiv preprint arXiv:2112.09118","author":"Izacard Gautier","year":"2021","unstructured":"Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2021. Unsupervised dense information retrieval with contrastive learning. arXiv preprint arXiv:2112.09118 (2021)."},{"key":"e_1_3_2_2_19_1","volume-title":"Bill Yuchen Lin, and Wenhu Chen","author":"Jiang Dongfu","year":"2023","unstructured":"Dongfu Jiang, Yishan Li, Ge Zhang, Wenhao Huang, Bill Yuchen Lin, and Wenhu Chen. 2023. TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks. ArXiv, Vol. abs\/2310.00752 (2023). https:\/\/api.semanticscholar.org\/CorpusID:263334281"},{"key":"e_1_3_2_2_20_1","volume-title":"Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. arXiv preprint arXiv:1705.03551","author":"Joshi Mandar","year":"2017","unstructured":"Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017. Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. arXiv preprint arXiv:1705.03551 (2017)."},{"key":"e_1_3_2_2_21_1","volume-title":"Juan Diego Rodriguez, and Greg Durrett","author":"Kamoi Ryo","year":"2023","unstructured":"Ryo Kamoi, Tanya Goyal, Juan Diego Rodriguez, and Greg Durrett. 2023. Wice: Real-world entailment for claims in wikipedia. arXiv preprint arXiv:2303.01432 (2023)."},{"key":"e_1_3_2_2_22_1","volume-title":"Dense passage retrieval for open-domain question answering. arXiv preprint arXiv:2004.04906","author":"Karpukhin Vladimir","year":"2020","unstructured":"Vladimir Karpukhin, Barlas Ouguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense passage retrieval for open-domain question answering. arXiv preprint arXiv:2004.04906 (2020)."},{"key":"e_1_3_2_2_23_1","volume-title":"Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa.","author":"Kojima Takeshi","year":"2022","unstructured":"Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. Large language models are zero-shot reasoners. Advances in neural information processing systems, Vol. 35 (2022), 22199--22213."},{"key":"e_1_3_2_2_24_1","volume-title":"Evaluating the factual consistency of abstractive text summarization. arXiv preprint arXiv:1910.12840","author":"Kry'sci'nski Wojciech","year":"2019","unstructured":"Wojciech Kry'sci'nski, Bryan McCann, Caiming Xiong, and Richard Socher. 2019. Evaluating the factual consistency of abstractive text summarization. arXiv preprint arXiv:1910.12840 (2019)."},{"key":"e_1_3_2_2_25_1","volume-title":"Aquamuse: Automatically generating datasets for query-based multi-document summarization. arXiv preprint arXiv:2010.12694","author":"Kulkarni Sayali","year":"2020","unstructured":"Sayali Kulkarni, Sheide Chammas, Wan Zhu, Fei Sha, and Eugene Ie. 2020. Aquamuse: Automatically generating datasets for query-based multi-document summarization. arXiv preprint arXiv:2010.12694 (2020)."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00276"},{"key":"e_1_3_2_2_27_1","volume-title":"Fast and Accurate Factual Inconsistency Detection Over Long Documents. arXiv preprint arXiv:2310.13189","author":"Lattimer Barrett Martin","year":"2023","unstructured":"Barrett Martin Lattimer, Patrick Chen, Xinyuan Zhang, and Yi Yang. 2023. Fast and Accurate Factual Inconsistency Detection Over Long Documents. arXiv preprint arXiv:2310.13189 (2023)."},{"key":"e_1_3_2_2_28_1","volume-title":"Evaluating verifiability in generative search engines. arXiv preprint arXiv:2304.09848","author":"Liu Nelson F","year":"2023","unstructured":"Nelson F Liu, Tianyi Zhang, and Percy Liang. 2023. Evaluating verifiability in generative search engines. arXiv preprint arXiv:2304.09848 (2023)."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599931"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.153"},{"key":"e_1_3_2_2_31_1","unstructured":"Jacob Menick Maja Trebacz Vladimir Mikulik John Aslanides Francis Song Martin Chadwick Mia Glaese Susannah Young Lucy Campbell-Gillingham Geoffrey Irving et al. 2022. Teaching language models to support answers with verified quotes. arXiv preprint arXiv:2203.11147 (2022)."},{"key":"e_1_3_2_2_32_1","volume-title":"Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozi\u00e8re, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al.","author":"Mialon Gr\u00e9goire","year":"2023","unstructured":"Gr\u00e9goire Mialon, Roberto Dess`i, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozi\u00e8re, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al. 2023. Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023)."},{"key":"e_1_3_2_2_33_1","volume-title":"Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi.","author":"Min Sewon","year":"2023","unstructured":"Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Wei Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2023. FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation. arXiv preprint arXiv:2305.14251 (2023)."},{"key":"e_1_3_2_2_34_1","unstructured":"Reiichiro Nakano Jacob Hilton Suchir Balaji Jeff Wu Long Ouyang Christina Kim Christopher Hesse Shantanu Jain Vineet Kosaraju William Saunders Xu Jiang Karl Cobbe Tyna Eloundou Gretchen Krueger Kevin Button Matthew Knight Benjamin Chess and John Schulman. 2022. WebGPT: Browser-assisted question-answering with human feedback. arxiv: 2112.09332 [cs.CL]"},{"key":"e_1_3_2_2_35_1","volume-title":"GPT-4 technical report. arXiv preprint arXiv:2303.08774","author":"AI.","year":"2023","unstructured":"OpenAI. 2023. GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023)."},{"key":"e_1_3_2_2_36_1","first-page":"27730","article-title":"Training language models to follow instructions with human feedback","volume":"35","author":"Ouyang Long","year":"2022","unstructured":"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, Vol. 35 (2022), 27730--27744.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.499"},{"key":"e_1_3_2_2_38_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_2_39_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00605"},{"key":"e_1_3_2_2_40_1","volume-title":"Is reinforcement learning (not) for natural language processing?: Benchmarks, baselines, and building blocks for natural language policy optimization. arXiv preprint arXiv:2210.01241","author":"Ramamurthy Rajkumar","year":"2022","unstructured":"Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kiant\u00e9 Brantley, Jack Hessel, Rafet Sifa, Christian Bauckhage, Hannaneh Hajishirzi, and Yejin Choi. 2022. Is reinforcement learning (not) for natural language processing?: Benchmarks, baselines, and building blocks for natural language policy optimization. arXiv preprint arXiv:2210.01241 (2022)."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00266"},{"key":"e_1_3_2_2_42_1","volume-title":"REPLUG: Retrieval-Augmented Black-Box Language Models. arxiv: 2301.12652 [cs.CL]","author":"Shi Weijia","year":"2023","unstructured":"Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen tau Yih. 2023. REPLUG: Retrieval-Augmented Black-Box Language Models. arxiv: 2301.12652 [cs.CL]"},{"key":"e_1_3_2_2_43_1","volume-title":"Stephen Roller, Megan Ung, Moya Chen, Kushal Arora, Joshua Lane, et al.","author":"Shuster Kurt","year":"2022","unstructured":"Kurt Shuster, Jing Xu, Mojtaba Komeili, Da Ju, Eric Michael Smith, Stephen Roller, Megan Ung, Moya Chen, Kushal Arora, Joshua Lane, et al. 2022. Blenderbot 3: a deployed conversational agent that continually learns to responsibly engage. arXiv preprint arXiv:2208.03188 (2022)."},{"key":"e_1_3_2_2_44_1","first-page":"5861","article-title":"Process for adapting language models to society (palms) with values-targeted datasets","volume":"34","author":"Solaiman Irene","year":"2021","unstructured":"Irene Solaiman and Christy Dennison. 2021. Process for adapting language models to society (palms) with values-targeted datasets. Advances in Neural Information Processing Systems, Vol. 34 (2021), 5861--5873.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_45_1","volume-title":"Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al.","author":"Thoppilan Romal","year":"2022","unstructured":"Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al. 2022. Lamda: Language models for dialog applications. arXiv preprint arXiv:2201.08239 (2022)."},{"key":"e_1_3_2_2_46_1","unstructured":"Hugo Touvron Louis Martin Kevin 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 preprint arXiv:2307.09288 (2023)."},{"key":"e_1_3_2_2_47_1","volume-title":"Evaluating open question answering evaluation. arXiv preprint arXiv:2305.12421","author":"Wang Cunxiang","year":"2023","unstructured":"Cunxiang Wang, Sirui Cheng, Zhikun Xu, Bowen Ding, Yidong Wang, and Yue Zhang. 2023. Evaluating open question answering evaluation. arXiv preprint arXiv:2305.12421 (2023)."},{"key":"e_1_3_2_2_48_1","unstructured":"Cunxiang Wang Xiaoze Liu Yuanhao Yue Xiangru Tang Tianhang Zhang Cheng Jiayang Yunzhi Yao Wenyang Gao Xuming Hu Zehan Qi et al. 2023. Survey on factuality in large language models: Knowledge retrieval and domain-specificity. arXiv preprint arXiv:2310.07521 (2023)."},{"key":"e_1_3_2_2_49_1","volume-title":"Neural text generation with unlikelihood training. arXiv preprint arXiv:1908.04319","author":"Welleck Sean","year":"2019","unstructured":"Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. 2019. Neural text generation with unlikelihood training. arXiv preprint arXiv:1908.04319 (2019)."},{"key":"e_1_3_2_2_50_1","volume-title":"Fine-Grained Human Feedback Gives Better Rewards for Language Model Training. arXiv preprint arXiv:2306.01693","author":"Wu Zeqiu","year":"2023","unstructured":"Zeqiu Wu, Yushi Hu, Weijia Shi, Nouha Dziri, Alane Suhr, Prithviraj Ammanabrolu, Noah A Smith, Mari Ostendorf, and Hannaneh Hajishirzi. 2023. Fine-Grained Human Feedback Gives Better Rewards for Language Model Training. arXiv preprint arXiv:2306.01693 (2023)."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671656"},{"key":"e_1_3_2_2_52_1","volume-title":"Preference-grounded Token-level Guidance for Language Model Fine-tuning. arXiv preprint arXiv:2306.00398","author":"Yang Shentao","year":"2023","unstructured":"Shentao Yang, Shujian Zhang, Congying Xia, Yihao Feng, Caiming Xiong, and Mingyuan Zhou. 2023. Preference-grounded Token-level Guidance for Language Model Fine-tuning. arXiv preprint arXiv:2306.00398 (2023)."},{"key":"e_1_3_2_2_53_1","volume-title":"AlignScore: Evaluating Factual Consistency with a Unified Alignment Function. arXiv preprint arXiv:2305.16739","author":"Zha Yuheng","year":"2023","unstructured":"Yuheng Zha, Yichi Yang, Ruichen Li, and Zhiting Hu. 2023. AlignScore: Evaluating Factual Consistency with a Unified Alignment Function. arXiv preprint arXiv:2305.16739 (2023)."}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3672065","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3672065","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:23Z","timestamp":1750291463000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3672065"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":53,"alternative-id":["10.1145\/3637528.3672065","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3672065","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}