{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T16:40:05Z","timestamp":1755880805237,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,4,26]]},"DOI":"10.1145\/3663976.3664023","type":"proceedings-article","created":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T18:25:58Z","timestamp":1719512758000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Exploring Synergies between Causal Models and LargeLanguage Models for Enhanced Understanding and Inference"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6390-714X","authenticated-orcid":false,"given":"Yaru","family":"Sun","sequence":"first","affiliation":[{"name":"The Third Research Institute of Ministry of Public Security, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2747-7721","authenticated-orcid":false,"given":"Ying","family":"Yang","sequence":"additional","affiliation":[{"name":"The Third Research Institute of Ministry of Public Security, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5376-0260","authenticated-orcid":false,"given":"Wenhao","family":"Fu","sequence":"additional","affiliation":[{"name":"The Third Research Institute of Ministry of Public Security, China"}]}],"member":"320","published-online":{"date-parts":[[2024,6,27]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Gpt-4 technical report. arXiv preprint arXiv:2303.08774","author":"Achiam Josh","year":"2023","unstructured":"Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia\u00a0Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)."},{"key":"e_1_3_2_1_2_1","volume-title":"From query tools to causal architects: Harnessing large language models for advanced causal discovery from data. arXiv preprint arXiv:2306.16902","author":"Ban Taiyu","year":"2023","unstructured":"Taiyu Ban, Lyvzhou Chen, Xiangyu Wang, and Huanhuan Chen. 2023. From query tools to causal architects: Harnessing large language models for advanced causal discovery from data. arXiv preprint arXiv:2306.16902 (2023)."},{"key":"e_1_3_2_1_3_1","volume-title":"Conference on Causal Learning and Reasoning. PMLR, 866\u2013879","author":"Beckers Sander","year":"2023","unstructured":"Sander Beckers, Joseph Halpern, and Christopher Hitchcock. 2023. Causal models with constraints. In Conference on Causal Learning and Reasoning. PMLR, 866\u2013879."},{"key":"e_1_3_2_1_4_1","volume-title":"Driving with llms: Fusing object-level vector modality for explainable autonomous driving. arXiv preprint arXiv:2310.01957","author":"Chen Long","year":"2023","unstructured":"Long Chen, Oleg Sinavski, Jan H\u00fcnermann, Alice Karnsund, Andrew\u00a0James Willmott, Danny Birch, Daniel Maund, and Jamie Shotton. 2023. Driving with llms: Fusing object-level vector modality for explainable autonomous driving. arXiv preprint arXiv:2310.01957 (2023)."},{"key":"e_1_3_2_1_5_1","volume-title":"LMPriors: Pre-Trained Language Models as Task-Specific Priors. arXiv preprint arXiv:2210.12530","author":"Choi Kristy","year":"2022","unstructured":"Kristy Choi, Chris Cundy, Sanjari Srivastava, and Stefano Ermon. 2022. LMPriors: Pre-Trained Language Models as Task-Specific Priors. arXiv preprint arXiv:2210.12530 (2022)."},{"key":"e_1_3_2_1_6_1","volume-title":"Prompt engineering for ChatGPT: a quick guide to techniques, tips, and best practices. Authorea Preprints","author":"Ekin Sabit","year":"2023","unstructured":"Sabit Ekin. 2023. Prompt engineering for ChatGPT: a quick guide to techniques, tips, and best practices. Authorea Preprints (2023)."},{"key":"e_1_3_2_1_7_1","volume-title":"Review of causal discovery methods based on graphical models. Frontiers in genetics 10","author":"Glymour Clark","year":"2019","unstructured":"Clark Glymour, Kun Zhang, and Peter Spirtes. 2019. Review of causal discovery methods based on graphical models. Frontiers in genetics 10 (2019), 524."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/info14070367"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-5709"},{"key":"e_1_3_2_1_10_1","unstructured":"Christopher Hitchcock. 2018. Causal models. (2018)."},{"key":"e_1_3_2_1_11_1","volume-title":"NeurIPS ML Safety Workshop.","author":"Hobbhahn Marius","year":"2022","unstructured":"Marius Hobbhahn, Tom Lieberum, and David Seiler. 2022. Investigating causal understanding in LLMs. In NeurIPS ML Safety Workshop."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1986.10478354"},{"key":"e_1_3_2_1_13_1","volume-title":"Let\u2019s verify step by step. arXiv preprint arXiv:2305.20050","author":"Harri Edwards-Bowen Baker Yura Burda","year":"2023","unstructured":"Yura Burda Harri Edwards-Bowen Baker Teddy Lee Jan Leike John Schulman Ilya Sutskever Karl\u00a0Cobbe Hunter\u00a0Lightman, Vineet\u00a0Kosaraju. 2023. Let\u2019s verify step by step. arXiv preprint arXiv:2305.20050 (2023)."},{"key":"e_1_3_2_1_14_1","volume-title":"Navigating the ocean of biases: Political bias attribution in language models via causal structures. arXiv preprint arXiv:2311.08605","author":"Jenny F","year":"2023","unstructured":"David\u00a0F Jenny, Yann Billeter, Mrinmaya Sachan, Bernhard Sch\u00f6lkopf, and Zhijing Jin. 2023. Navigating the ocean of biases: Political bias attribution in language models via causal structures. arXiv preprint arXiv:2311.08605 (2023)."},{"key":"e_1_3_2_1_15_1","volume-title":"Can Large Language Models Infer Causation from Correlation?arXiv preprint arXiv:2306.05836","author":"Jin Zhijing","year":"2023","unstructured":"Zhijing Jin, Jiarui Liu, Zhiheng Lyu, Spencer Poff, Mrinmaya Sachan, Rada Mihalcea, Mona Diab, and Bernhard Sch\u00f6lkopf. 2023. Can Large Language Models Infer Causation from Correlation?arXiv preprint arXiv:2306.05836 (2023)."},{"key":"e_1_3_2_1_16_1","volume-title":"Efficient causal graph discovery using large language models. arXiv preprint arXiv:2402.01207","author":"Jiralerspong Thomas","year":"2024","unstructured":"Thomas Jiralerspong, Xiaoyin Chen, Yash More, Vedant Shah, and Yoshua Bengio. 2024. Efficient causal graph discovery using large language models. arXiv preprint arXiv:2402.01207 (2024)."},{"key":"e_1_3_2_1_17_1","volume-title":"Causal reasoning and large language models: Opening a new frontier for causality. arXiv preprint arXiv:2305.00050","author":"K\u0131c\u0131man Emre","year":"2023","unstructured":"Emre K\u0131c\u0131man, Robert Ness, Amit Sharma, and Chenhao Tan. 2023. Causal reasoning and large language models: Opening a new frontier for causality. arXiv preprint arXiv:2305.00050 (2023)."},{"key":"e_1_3_2_1_18_1","volume-title":"Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461","author":"Lewis Mike","year":"2019","unstructured":"Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019)."},{"key":"e_1_3_2_1_19_1","volume-title":"Pretrained language models for text generation: A survey. arXiv preprint arXiv:2201.05273","author":"Li Junyi","year":"2022","unstructured":"Junyi Li, Tianyi Tang, Wayne\u00a0Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. 2022. Pretrained language models for text generation: A survey. arXiv preprint arXiv:2201.05273 (2022)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00110"},{"key":"e_1_3_2_1_21_1","volume-title":"Causal discovery with language models as imperfect experts. arXiv preprint arXiv:2307.02390","author":"Long Stephanie","year":"2023","unstructured":"Stephanie Long, Alexandre Pich\u00e9, Valentina Zantedeschi, Tibor Schuster, and Alexandre Drouin. 2023. Causal discovery with language models as imperfect experts. arXiv preprint arXiv:2307.02390 (2023)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3400051.3400058"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3352100"},{"key":"e_1_3_2_1_24_1","unstructured":"Nick Pawlowski James Vaughan Joel Jennings and Cheng Zhang. 2023. Answering causal questions with augmented llms. (2023)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Bernhard Sch\u00f6lkopf. 2022. Causality for machine learning. In Probabilistic and Causal Inference: The Works of Judea Pearl. 765\u2013804.","DOI":"10.1145\/3501714.3501755"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-5827"},{"key":"e_1_3_2_1_27_1","first-page":"841","article-title":"Counterfactual explanations without opening the black box: Automated decisions and the GDPR","volume":"31","author":"Wachter Sandra","year":"2017","unstructured":"Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2017. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harv. JL & Tech. 31 (2017), 841.","journal-title":"Harv. JL & Tech."},{"key":"e_1_3_2_1_28_1","first-page":"24824","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume":"35","author":"Wei Jason","year":"2022","unstructured":"Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc\u00a0V Le, Denny Zhou, 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems 35 (2022), 24824\u201324837.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_29_1","volume-title":"KG-BERT: BERT for knowledge graph completion. arXiv preprint arXiv:1909.03193","author":"Yao Liang","year":"2019","unstructured":"Liang Yao, Chengsheng Mao, and Yuan Luo. 2019. KG-BERT: BERT for knowledge graph completion. arXiv preprint arXiv:1909.03193 (2019)."},{"key":"e_1_3_2_1_30_1","volume-title":"Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems 36","author":"Yao Shunyu","year":"2024","unstructured":"Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2024. Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems 36 (2024)."},{"key":"e_1_3_2_1_31_1","volume-title":"Cumulative reasoning with large language models. arXiv preprint arXiv:2308.04371","author":"Andrew Chi-Chih\u00a0Yao Yang Yuan","year":"2023","unstructured":"Yang Yuan Andrew Chi-Chih\u00a0Yao Yifan\u00a0Zhang, Jingqin\u00a0Yang. 2023. Cumulative reasoning with large language models. arXiv preprint arXiv:2308.04371 (2023)."},{"key":"e_1_3_2_1_32_1","volume-title":"Survey of Causal Inference for Knowledge Graphs and Large Language Models.Journal of Frontiers of Computer Science & Technology 17, 10","author":"Yuan LI","year":"2023","unstructured":"LI Yuan, MA Xinyu, YANG Guoli, ZHAO Huiqun, and SONG Wei. 2023. Survey of Causal Inference for Knowledge Graphs and Large Language Models.Journal of Frontiers of Computer Science & Technology 17, 10 (2023)."}],"event":{"name":"CVIPPR 2024: 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition","acronym":"CVIPPR 2024","location":"Xiamen China"},"container-title":["Proceedings of the 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3663976.3664023","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3663976.3664023","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T16:27:59Z","timestamp":1755880079000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3663976.3664023"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,26]]},"references-count":32,"alternative-id":["10.1145\/3663976.3664023","10.1145\/3663976"],"URL":"https:\/\/doi.org\/10.1145\/3663976.3664023","relation":{},"subject":[],"published":{"date-parts":[[2024,4,26]]},"assertion":[{"value":"2024-06-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}