{"type":"ScholarlyArticle","title":"FactCHD: Benchmarking Fact-Conflicting Hallucination Detection","authors":[{"name":"Xiang Chen","slug":"xiang-chen","given":"Xiang","family":"Chen","affiliation":"College of Computer Science and Technology , Zhejiang University; Zhejiang University -Ant Group Joint Research Center for Knowledge Graphs {xiang_chen , sdz ,"},{"name":"Duanzheng Song","slug":"duanzheng-song","given":"Duanzheng","family":"Song","affiliation":"School of Software Technology , Zhejiang University"},{"name":"Honghao Gui","slug":"honghao-gui","given":"Honghao","family":"Gui","affiliation":"College of Computer Science and Technology , Zhejiang University; Zhejiang University -Ant Group Joint Research Center for Knowledge Graphs {xiang_chen , sdz ,"},{"name":"Chenxi Wang","slug":"chenxi-wang","given":"Chenxi","family":"Wang","affiliation":"School of Software Technology , Zhejiang University; Zhejiang University -Ant Group Joint Research Center for Knowledge Graphs {xiang_chen , sdz ,"},{"name":"Ningyu Zhang","slug":"ningyu-zhang","given":"Ningyu","family":"Zhang","affiliation":"School of Software Technology , Zhejiang University; Zhejiang University -Ant Group Joint Research Center for Knowledge Graphs {xiang_chen , sdz ,"},{"name":"Yong Jiang","slug":"yong-jiang","given":"Yong","family":"Jiang","affiliation":"Alibaba Group"},{"name":"Fei Huang","slug":"fei-huang","given":"Fei","family":"Huang","affiliation":"Alibaba Group"},{"name":"Chengfei Lyu","slug":"chengfei-lyu","given":"Chengfei","family":"Lyu","affiliation":"Alibaba Group"},{"name":"Dan Zhang","slug":"dan-zhang","given":"Dan","family":"Zhang","affiliation":"School of Software Technology , Zhejiang University"},{"name":"Huajun Chen","slug":"huajun-chen","given":"Huajun","family":"Chen","affiliation":"College of Computer Science and Technology , Zhejiang University; Zhejiang University -Ant Group Joint Research Center for Knowledge Graphs {xiang_chen , sdz ,"}],"abstract":"Despite their impressive generative capabilities, LLMs are hindered by fact-conflicting hallucinations in real-world applications. The accurate identification of hallucinations in texts generated by LLMs, especially in complex inferential scenarios, is a relatively unexplored area. To address this gap, we present FactCHD, a dedicated benchmark designed for the detection of fact-conflicting hallucinations from LLMs. FactCHD features a diverse dataset that spans various factuality patterns, including vanilla, multi-hop, comparison, and set operation. A distinctive element of FactCHD is its integration of fact-based evidence chains, significantly enhancing the depth of evaluating the detectors' explanations. Experiments on different LLMs expose the shortcomings of current approaches in detecting factual errors accurately. Furthermore, we introduce TRUTH-TRIANGULATOR which synthesizes reflective considerations by tool-enhanced ChatGPT and LoRA-tuning based on Llama2, aiming to yield more credible detection through the amalgamation of predictive results and evidence.","doi":"10.24963/ijcai.2024/687","url":"https://ijcai.org/proceedings/2024/687","pdf":"https://ijcai.org/proceedings/2024/0687.pdf","markdown":"https://ijcai.org/proceedings/2024/687.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/687","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":687,"pages":{"first":6216,"last":6224},"keywords":["Natural Language Processing: NLP: Applications","Natural Language Processing: NLP: Resources and evaluation"],"videos":[],"references":[{"position":1,"raw":"[Aly et al., 2021] Rami Aly, Zhijiang Guo, Michael Sejr Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, and Arpit Mittal. FEVEROUS: fact extraction and verification over unstruc- tured and structured information. In Joaquin Vanschoren and Sai-Kit Yeung, editors, Proceedings of the Neural Infor- mation Processing Systems Track on Datasets and Bench- marks 1, NeurIPS Datasets and Benchmarks 2021, Decem- ber 2021, virtual, 2021.","title":"FEVEROUS: fact extraction and verification over unstructured and structured information","authors":"Rami Aly, Zhijiang Guo, Sejr Michael, James Schlichtkrull, Andreas Thorne, Christos Vlachos, Oana Christodoulopoulos, Arpit Cocarascu, Mittal","year":"2021","venue":"Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chandak et al., 2023] Payal Chandak, Kexin Huang, and Marinka Zitnik. Building a knowledge graph to enable precision medicine. Scientific Data, 10(1):67, 2023.","title":"Building a knowledge graph to enable precision medicine","authors":"Payal Chandak, Kexin Huang, Marinka Zitnik","year":"2023","venue":"Scientific Data","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2020] Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang. Tabfact: A large-scale dataset for table-based fact verification. In ICLR 2020. OpenRe- view.net, 2020.","title":"Tabfact: A large-scale dataset for table-based fact verification","authors":"Chen","year":"2020","venue":"ICLR 2020","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2022] Xiang Chen, Ningyu Zhang, Xin Xie, Shumin Deng, Yunzhi Yao, Chuanqi Tan, Fei Huang, Luo Si, and Huajun Chen. Knowprompt: Knowledge-aware prompt-tuning with synergistic optimization for relation extraction. In WWW 2022, pages 2778-2788. ACM, 2022.","title":"Knowprompt: Knowledge-aware prompt-tuning with synergistic optimization for relation extraction","authors":"Xiang Chen, Ningyu Zhang, Xin Xie, Shumin Deng, Yunzhi Yao, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen","year":"2022","venue":"WWW 2022","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chern et al., 2023] I-Chun Chern, Steffi Chern, Shiqi Chen, Weizhe Yuan, Kehua Feng, Chunting Zhou, Junxian He, Graham Neubig, and Pengfei Liu. Factool: Factuality detection in generative AI -A tool augmented frame- work for multi-task and multi-domain scenarios. CoRR, abs/2307.13528, 2023.","title":"Factool: Factuality detection in generative AI -A tool augmented framework for multi-task and multi-domain scenarios","authors":"Chern","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chiang et al., 2023] Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023.","title":"Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality","authors":"Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, Ion Stoica, Eric P Xing","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Creswell and Shanahan, 2022] Antonia Creswell and Mur- ray Shanahan. Faithful reasoning using large language models. arXiv preprint arXiv:2208.14271, 2022.","title":"Faithful reasoning using large language models","authors":"Antonia Creswell, Murray Shanahan","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Das et al., 2023] Souvik Das, Sougata Saha, and Rohini K Srihari. Diving deep into modes of fact hallucinations in dialogue systems. arXiv preprint arXiv:2301.04449, 2023.","title":"Diving deep into modes of fact hallucinations in dialogue systems","authors":"Souvik Das, Sougata Saha, Rohini K Srihari","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Dhingra et al., 2019] Bhuwan Dhingra, Manaal Faruqui, Ankur P. Parikh, Ming-Wei Chang, Dipanjan Das, and William W. Cohen. Handling divergent reference texts when evaluating table-to-text generation. In Anna Korho- nen, David R. Traum, and Lluís Màrquez, editors, ACL 2019, pages 4884-4895. Association for Computational Linguistics, 2019.","title":"Handling divergent reference texts when evaluating table-to-text generation","authors":"Bhuwan Dhingra, Manaal Faruqui, P Ankur, Ming-Wei Parikh, Dipanjan Chang, William W Das, Cohen","year":"2019","venue":"ACL 2019","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Diggelmann et al., 2020] Thomas Diggelmann, Jordan Boyd-Graber, Jannis Bulian, Massimiliano Ciaramita, and Markus Leippold. Climate-fever: A dataset for verification of real-world climate claims. arXiv preprint arXiv:2012.00614, 2020.","title":"Climate-fever: A dataset for verification of real-world climate claims","authors":"Thomas Diggelmann, Jordan Boyd-Graber, Jannis Bulian, Massimiliano Ciaramita, Markus Leippold","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Dziri et al., 2022] Nouha Dziri, Ehsan Kamalloo, Sivan Mil- ton, Osmar R. Zaïane, Mo Yu, Edoardo Maria Ponti, and Siva Reddy. Faithdial: A faithful benchmark for information-seeking dialogue. Trans. Assoc. Comput. Lin- guistics, 10:1473-1490, 2022.","title":"Faithdial: A faithful benchmark for information-seeking dialogue","authors":"Nouha Dziri, Ehsan Kamalloo, Sivan Milton, Osmar R Zaïane, Mo Yu, Edoardo, Maria Ponti, Siva Reddy","year":"2022","venue":"Trans. Assoc. Comput. Linguistics","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Gupta et al., 2022] Prakhar Gupta, Chien-Sheng Wu, Wen- hao Liu, and Caiming Xiong. Dialfact: A benchmark for fact-checking in dialogue. In ACL 2022, pages 3785-3801, 2022.","title":"Dialfact: A benchmark for fact-checking in dialogue","authors":"Prakhar Gupta, Chien-Sheng Wu, Wenhao Liu, Caiming Xiong","year":"2022","venue":"ACL 2022","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hu et al., 2022] Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu 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.","title":"Lora: Low-rank adaptation of large language models","authors":"Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen","year":"2022","venue":"The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Huang et al., 2023] Yue Huang, Qihui Zhang, Philip S. Yu, and Lichao Sun. Trustgpt: A benchmark for trust- worthy and responsible large language models. CoRR, abs/2304.10513, 2023.","title":"Trustgpt: A benchmark for trustworthy and responsible large language models","authors":"Yue Huang, Qihui Zhang, Philip S Yu, Lichao Sun","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Ji et al., 2023] Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12):1-38, 2023.","title":"Survey of hallucination in natural language generation","authors":"Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye, Jin Bang, Andrea Madotto, Pascale Fung","year":"2023","venue":"ACM Computing Surveys","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Jiang et al., 2020] Yichen Jiang, Shikha Bordia, Zheng Zhong, Charles Dognin, Maneesh Kumar Singh, and Mohit Bansal. Hover: A dataset for many-hop fact extraction and claim verification. In Findings of EMNLP 2020, 2020.","title":"Hover: A dataset for many-hop fact extraction and claim verification","authors":"Yichen Jiang, Shikha Bordia, Zheng Zhong, Charles Dognin, Maneesh Kumar Singh, Mohit Bansal","year":"2020","venue":"Findings of EMNLP 2020","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kang et al., 2024] Mintong Kang, Nezihe Merve Gürel, Ning Yu, Dawn Song, and Bo Li. C-RAG: certified genera- tion risks for retrieval-augmented language models. CoRR, abs/2402.03181, 2024.","title":"C-RAG: certified generation risks for retrieval-augmented language models","authors":"Kang","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Kryściński et al., 2020] Wojciech Kryściński, Bryan Mc- Cann, Caiming Xiong, and Richard Socher. Evaluating the factual consistency of abstractive text summarization. In EMNLP, pages 9332-9346, 2020.","title":"Evaluating the factual consistency of abstractive text summarization","authors":"Wojciech Kryściński, Bryan Mc-Cann, Caiming Xiong, Richard Socher","year":"2020","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Li et al., 2023] Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. Halueval: A large-scale hallucination evaluation benchmark for large language mod- els. CoRR, abs/2305.11747, 2023.","title":"Halueval: A large-scale hallucination evaluation benchmark for large language models","authors":"Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Liang et al., 2023] Ke Liang, Sihang Zhou, Yue Liu, Lingyuan Meng, Meng Liu, and Xinwang Liu. Structure guided multi-modal pre-trained transformer for knowledge graph reasoning. CoRR, abs/2307.03591, 2023.","title":"Structure guided multi-modal pre-trained transformer for knowledge graph reasoning","authors":"Ke Liang, Sihang Zhou, Yue Liu, Lingyuan Meng, Meng Liu, Xinwang Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Liang et al., 2024] Ke Liang, Yue Liu, Sihang Zhou, Wenx- uan Tu, Yi Wen, Xihong Yang, Xiangjun Dong, and Xin- wang Liu. Knowledge graph contrastive learning based on relation-symmetrical structure. IEEE Trans. Knowl. Data Eng., 36(1):226-238, 2024.","title":"Knowledge graph contrastive learning based on relation-symmetrical structure","authors":"Ke Liang, Yue Liu, Sihang Zhou, Wenxuan Tu, Yi Wen, Xihong Yang, Xiangjun Dong, Xinwang Liu","year":"2024","venue":"IEEE Trans. Knowl. Data Eng","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Mallen et al., 2022] Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Hannaneh Hajishirzi, and Daniel Khashabi. When not to trust language models: Investigating effec- tiveness and limitations of parametric and non-parametric memories. arXiv preprint arXiv:2212.10511, 2022.","title":"When not to trust language models: Investigating effectiveness and limitations of parametric and non-parametric memories","authors":"Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Hannaneh Hajishirzi, Daniel Khashabi","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Mohr et al., 2022] Isabelle Mohr, Amelie Wührl, and Ro- man Klinger. Covert: A corpus of fact-checked biomedical COVID-19 tweets. In LREC 2022, pages 244-257, 2022.","title":"Covert: A corpus of fact-checked biomedical COVID-19 tweets","authors":"Isabelle Mohr, Amelie Wührl, Roman Klinger","year":"2022","venue":"LREC 2022","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Muhlgay et al., 2023] Dor Muhlgay, Ori Ram, Inbal Magar, Yoav Levine, Nir Ratner, Yonatan Belinkov, Omri Abend, Kevin Leyton-Brown, Amnon Shashua, and Yoav Shoham. Generating benchmarks for factuality evaluation of lan- guage models. CoRR, abs/2307.06908, 2023.","title":"Generating benchmarks for factuality evaluation of language models","authors":"Dor Muhlgay, Ori Ram, Inbal Magar, Yoav Levine, Nir Ratner, Yonatan Belinkov, Omri Abend, Kevin Leyton-Brown, Amnon Shashua, Yoav Shoham","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[OpenAI, 2022] OpenAI. Chatgpt: Optimizing language models for dialogue. https://openai.com/blog/chatgpt, 2022.","title":"OpenAI. Chatgpt: Optimizing language models for dialogue","authors":"Openai","year":"2022","venue":null,"doi":null,"url":"https://openai.com/blog/chatgpt,2022","internal_url":null},{"position":26,"raw":"[Pagnoni et al., 2021] Artidoro Pagnoni, Vidhisha Balachan- dran, and Yulia Tsvetkov. Understanding factuality in abstractive summarization with frank: A benchmark for factuality metrics. In NAACL, pages 4812-4829, 2021.","title":"Understanding factuality in abstractive summarization with frank: A benchmark for factuality metrics","authors":"Pagnoni","year":"2021","venue":"NAACL","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Qin et al., 2023] Yujia Qin, Shengding Hu, and et al. Tool learning with foundation models. CoRR, abs/2304.08354, 2023.","title":"Tool learning with foundation models","authors":"Yujia Qin, Shengding Hu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Rashkin et al., 2021] Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, Michael Collins, Dipanjan Das, Slav Petrov, Gaurav Singh Tomar, Iulia Turc, and David Re- itter. Measuring attribution in natural language generation models. CoRR, abs/2112.12870, 2021.","title":"Measuring attribution in natural language generation models","authors":"Vitaly Hannah Rashkin, Matthew Nikolaev, Michael Lamm, Dipanjan Collins, Slav Das, Gaurav Petrov, Iulia Singh Tomar, David Turc, Reitter","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Reimers and Gurevych, 2019] Nils Reimers and Iryna Gurevych. Sentence-bert: Sentence embeddings using siamese bert-networks. In EMNLP-IJCNLP 2019, pages 3980-3990, 2019.","title":"Sentence-bert: Sentence embeddings using siamese bert-networks","authors":"Gurevych Reimers, Nils Reimers, Iryna Gurevych","year":"2019","venue":"EMNLP-IJCNLP 2019","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Saakyan et al., 2021] Arkadiy Saakyan, Tuhin Chakrabarty, and Smaranda Muresan. Covid-fact: Fact extraction and verification of real-world claims on COVID-19 pandemic. In ACL/IJCNLP 2021, pages 2116-2129, 2021.","title":"Covid-fact: Fact extraction and verification of real-world claims on COVID-19 pandemic","authors":"Arkadiy Saakyan, Tuhin Chakrabarty, Smaranda Muresan","year":"2021","venue":"ACL/IJCNLP 2021","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Sarrouti et al., 2021] Mourad Sarrouti, Asma Ben Abacha, Yassine Mrabet, and Dina Demner-Fushman. Evidence- based fact-checking of health-related claims. In Findings EMNLP 2021, pages 3499-3512, 2021.","title":"Evidencebased fact-checking of health-related claims","authors":"Mourad Sarrouti, Asma Ben Abacha, Yassine Mrabet, Dina Demner-Fushman","year":"2021","venue":"Findings EMNLP 2021","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Shuster et al., 2021] Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. Retrieval augmentation re- duces hallucination in conversation. In Findings of EMNLP 2021, 2021.","title":"Retrieval augmentation reduces hallucination in conversation","authors":"Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, Jason Weston","year":"2021","venue":"Findings of EMNLP 2021","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Taori et al., 2023] Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. Stanford alpaca: An instruction-following llama model. https://github.com/ tatsu-lab/stanford_alpaca, 2023.","title":"Stanford alpaca: An instruction-following llama model","authors":"Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, Tatsunori B Hashimoto","year":null,"venue":null,"doi":null,"url":"https://github.com/tatsu-lab/stanford_alpaca,2023","internal_url":null},{"position":34,"raw":"[Thorne et al., 2018] James Thorne, Andreas Vlachos, Chris- tos Christodoulopoulos, and Arpit Mittal. FEVER: a large-scale dataset for fact extraction and VERification. In NAACL (Long Papers), pages 809-819, New Orleans, Louisiana, June 2018.","title":"FEVER: a large-scale dataset for fact extraction and VERification","authors":"Thorne","year":"2018","venue":"NAACL (Long Papers)","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Touvron et al., 2023] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models. CoRR, abs/2302.13971, 2023.","title":"Llama: Open and efficient foundation language models","authors":"Touvron","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Valenza, 2016] Joyce Valenza. Truth, truthiness, triangu- lation: A news literacy toolkit for a \"post-truth\" world. School Library journal, 2016.","title":"Truth, truthiness, triangulation: A news literacy toolkit for a \"post-truth\" world","authors":"Joyce Valenza","year":"2016","venue":"School Library journal","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Vrandecic and Krötzsch, 2014] Denny Vrandecic and Markus Krötzsch. Wikidata: a free collaborative knowledgebase. Commun. ACM, 57(10):78-85, 2014.","title":"Wikidata: a free collaborative knowledgebase","authors":"Krötzsch Vrandecic, Denny Vrandecic, Markus Krötzsch","year":"2014","venue":"Commun. ACM","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wadden et al., 2020a] David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Co- han, and Hannaneh Hajishirzi. Fact or fiction: Verifying scientific claims. In EMNLP 2020, pages 7534-7550, 2020.","title":"Fact or fiction: Verifying scientific claims","authors":"Wadden","year":"2020","venue":"EMNLP 2020","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wadden et al., 2020b] David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, and Hannaneh Hajishirzi. Fact or fiction: Verifying scien- tific claims. In Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu, editors, EMNLP, pages 7534-7550, Online, November 2020.","title":"Fact or fiction: Verifying scientific claims","authors":"Wadden","year":"2020","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wang et al., 2023a] Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, Xiangru Tang, and et al. Survey on factuality in large language models: Knowledge, retrieval and domain- specificity. CoRR, abs/2310.07521, 2023.","title":"Survey on factuality in large language models: Knowledge, retrieval and domainspecificity","authors":"Wang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Wang et al., 2023b] Yike Wang, Shangbin Feng, Heng Wang, Weijia Shi, Vidhisha Balachandran, Tianxing He, and Yulia Tsvetkov. Resolving knowledge conflicts in large language models. CoRR, abs/2310.00935, 2023.","title":"Resolving knowledge conflicts in large language models","authors":"Wang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Yao et al., 2023] Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, and Ningyu Zhang. Editing large language models: Problems, methods, and opportunities. EMNLP 2023, abs/2305.13172, 2023.","title":"Editing large language models: Problems, methods, and opportunities","authors":"Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang","year":"2023","venue":"EMNLP 2023","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Yin et al., 2023] Zhangyue Yin, Qiushi Sun, Qipeng Guo, Jiawen Wu, Xipeng Qiu, and Xuanjing Huang. Do large language models know what they don't know? CoRR, abs/2305.18153, 2023.","title":"Do large language models know what they don't know?","authors":"Yin","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhang et al., 2023] Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, Longyue Wang, Anh Tuan Luu, Wei Bi, Freda Shi, and Shuming Shi. Siren's song in the AI ocean: A survey on hallucination in large language models. CoRR, abs/2309.01219, 2023.","title":"Siren's song in the AI ocean: A survey on hallucination in large language models","authors":"Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, Longyue Wang, Anh Tuan Luu, Wei Bi, Freda Shi, Shuming Shi","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhao et al., 2023] Wayne Xin Zhao, Kun Zhou, and et al. A survey of large language models. CoRR, abs/2303.18223, 2023.","title":"A survey of large language models","authors":"Kun Wayne Xin Zhao, Zhou","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}