| CARVIEW |
Updates
09/2025: FEVER9 Announced
We are pleased to announce that FEVER9 will be co-located with EACL 2026. In this year's workshop, we will introduce a new shared task focused on automated fact-checking (AFC) for image-text claims with evidence from the web. To learn more about the task read the dataset description paper AVerImaTeC: A Dataset for Automatic Verification of Image-Text Claims with Evidence from the Web, and go to the shared task webpage. You can find the call for papers in our workshop page.
About
With billions of individual pages on the web providing information on almost every conceivable topic, we should have the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this information is contained in structured sources (Wikidata, Freebase, etc.) – we are therefore limited by our ability to transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot of recent research and media coverage: false information coming from unreliable sources. [1]
The FEVER workshops are a venue for work in verifiable knowledge extraction and to stimulate progress in this direction.
Key Dates
In order to bring together researchers working on the various tasks related to fact extraction and verification, we will host a workshop welcoming submissions on related topics such as recognizing textual entailment, question answering and argumentation mining.
Workshop paper submissions:
- Submission deadline: Oct 6, 2025
- Direct paper submission deadline: Dec 19, 2025
- Commitment deadline (for pre-reviewed papers): Jan 7, 2026
- Notification: Jan 23, 2026
- Camera-ready: Feb 3, 2026
- Workshop: March 28 or 29, 2026 (Co-located with EACL 2026)
All deadlines are calculated at 11:59pm Anywhere on Earth (UTC-12).
Workshop Organising Committee
Mubashara Akhtar
King's College London
Mubashara Akhtar
Mubashara Akhtar is a PhD student at King's College London, working on multimodal fact-checking and supervised by Oana Cocarascu and Elena Simperl. She co-organized a WiML workshop co-located with NeurIPS 2020 and served as reviewer for the WebConf, AAAI, and ACL-affiliated conferences.
Rami Aly
University of Cambridge
Rami Aly
Rami Aly is a PhD student at Cambridge University, supervised by Andreas Vlachos and working on automated fact checking. He has previously co-organized a shared task on the hierarchical classification of book blurbs at GermEval 2019, a workshop co-located with KONVENS 2019. The dataset used for this shared task was created as part of his bachelor's thesis.
Rui Cao
University of Cambridge
Rui Cao
Rui Cao is a postdoctoral candidate at the University of Cambridge, working with Prof. Andreas Vlachos on multimodal fact-checking. She received her PhD in Singapore Management University, supervised by Prof. Jing Jiang. Her research interest is vision-language understanding, with a specific focus on online misbehavior and misinformation understanding with image-text and visual question answering.
Yulong Chen
University of Cambridge
Yulong Chen
Yulong Chen is a Postdoctoral Research Associate at the University of Cambridge, working with Professor Andreas Vlachos on textual claim verification. Yulong received his D.Phil. degree from Westlake University and Zhejiang University, advised by Professor Yue Zhang, working on text summarization. Before that, he obtained his M.Sc degree from the University of Edinburgh, where he was advised by Professor Bonnie Webber and worked on event relation extraction, and his B.Eng. degree from Wuhan University.
Oana Cocarascu
King's College London
Oana Cocarascu
Dr Oana Cocarascu is a Lecturer in Artificial Intelligence at King's College London. Her work is on applied research, specifically on how AI can be deployed to support real world applications. She received her PhD from Imperial College London, where she worked at the intersection of natural language processing and machine learning for argument mining. She also worked on the automatic extraction of argumentation frameworks from data to provide user-centric explanations in a variety of settings. Application areas span recommender systems, explainable classifiers, as well as safe and trusted AI systems.
Zhenyun Deng
University of Cambridge
Zhenyun Deng
Zhenyun Deng is a Postdoctoral Research Associate at the University of Cambridge, working with Andreas Vlachos on automated fact checking. His research focuses on the interpretability of models for NLP, including for fact verification, and question answering. Zhenyun received his PhD from the University of Auckland in 2023. He has served as a AC for ACL, EMNLP, NAACL.
Zifeng Ding
University of Cambridge
Zifeng Ding
Zifeng Ding is a Postdoctoral Researcher at the University of Cambridge, working with Andreas Vlachos in the Cambridge NLIP Group. His research interests include but are not limited to agentic AI, temporal reasoning with LLMs, multimodal fact-checking, and LLM hallucination detection/mitigation. Zifeng received his PhD from Ludwig Maximilian University of Munich in 2025.
Zhijiang Guo
HKUST (GZ)
Zhijiang Guo
Dr. Zhijiang Guo is an Assistant Professor at HKUST (GZ) and an Affiliated Assistant Professor at HKUST. Previously, he was a Postdoctoral Researcher at the University of Cambridge. He earned his Ph.D. from SUTD, with a visiting student stint at the University of Edinburgh. He has published in top conferences and journals like ICML, NeurIPS, ICLR, COLM, TACL, ACL, EMNLP, and NAACL. He has served as an Area Chair for NeurIPS, ICLR, *CL conferences, a Senior Program Committee member for AAAI and IJCAI, and an Action Editor for the ACL Rolling Review.
Arpit Mittal
Meta
Arpit Mittal
Dr Arpit Mittal is the Machine Learning lead for Facebook's team focusing on preventing behaviors on the platform leading to physical or emotional harm to the users. This includes problems involving Child Safety, Bullying and Harassment, Health Misinformation and Extreme Personal harm (Suicide and Self Injury, Non-Consensual Intimate Imagery). Before joining Facebook, he was a Senior Machine Learning Scientist at Amazon Alexa and worked on projects involving knowledge extraction, information retrieval and question answering. He received his PhD from the University of Oxford in Computer Vision and Machine Learning. He has been part of the organising committees for various major Natural Language Processing and Machine Learning conferences. Apart from the FEVER workshops, Arpit is also part of the founding committee of the Truth and Trust Online Conference.
Michael Schlichtkrull
Queen Mary University of London
Michael Schlichtkrull
Michael Schlichtkrull is a lecturer at Queen Mary University of London, and an affiliated lecturer at the University of Cambridge. His focus is on the modelling of structured data for NLP tasks, including for relational link prediction, fact verification, and question answering. Michael received his PhD from the University of Amsterdam in 2021. During the last few years he visited at the University of Edinburgh, where he worked on graph neural networks for relational link prediction and question answering, as well as interpretability for graph neural networks.
James Thorne
KAIST AI
James Thorne
James is Assistant Professor at KAIST AI Graduate School, South Korea, working on large-scale and knowledge-intensive natural language understanding. James completed his PhD at the University of Cambridge where he developed models and methods for automated fact verification and correction. James has also spent time at Amazon Alexa and Facebook AI Integrity and has served at the FEVER workshop since 2018.
Chenxi Whitehouse
Meta
Chenxi Whitehouse
Chenxi Whitehouse is a research scientist at Meta, focusing on Fundamental AI Research for LLMs. She previously worked as a postdoctoral researcher with Prof. Andreas Vlachos at the University of Cambridge and as an applied research scientist at Amazon AGI. Chenxi holds a PhD in knowledge-grounded NLP from City, University of London, and degrees in Electrical Engineering from the University of Erlangen-Nürnberg and University College London, and Information Engineering from Xi'an Jiaotong University.
Andreas Vlachos
University of Cambridge
Andreas Vlachos
Andreas is a Senior Lecturer at the University of Cambridge, working on the intersection of Natural Language Processing and Machine Learning. He has acted as an area co-chair for EACL 2017, EMNLP 2017, ACL 2019, EMNLP 2019 and CoNLL 2019 and as a senior area chair for Coling 2018. Vlachos's work on automated verification has been covered by international media including the New York Times and he has been invited to speak on the topic to a number of public events such as the Internet Governance Forum. Apart from the FEVER 2018, 2019 and 2020 he has also organised the 3rd Structured Prediction in NLP collocated with NAACL 2019