Call For Papers

With billions of individual pages on the web providing information on almost every conceivable topic, we should have the ability to reason about in a wide range of domains. However, in order to do so, we need to ensure that we trust the accuracy of the sources of information that we use. Handling false information coming from unreliable sources has become the focus of a lot of recent research and media coverage. In an effort to jointly address these problems, we are organizing the 9th instalment of the Fact Extraction and VERification (FEVER) workshop (http://fever.ai/) to promote research in this area. The workshop will be co-located with EACL 2026 and will be held either in Rabat, Morocco with the option of online attendance.

New Shared Task: In this year’s workshop, we will organise a new shared task focused on AVerImaTeC: A Dataset for Automatic Verification of Image-Text Claims with Evidence from the Web. It will consist of 1,297 real-world image-text claims that are fact checked using evidence from the web. Each claim is annotated with question-answer pairs supported by evidence (both images and text) available online, as well as textual justifications explaining how the evidence combines to produce a verdict. Given the multimodal nature of the task, both questions and answers may involve images. For each claim, systems must return a label (Supported, Refuted, Not Enough Evidence, Conflicting Evidence/Cherry-picking) and appropriate evidence. The evidence must be retrieved from the document and image collection provided by the organisers, see our shared task page. Please submit your papers here.

The timeline for it is as follows:

  • Training/dev data release: September 29, 2025
  • Test data release: November 28, 2025
  • Shared task system closes: December 2, 2025
  • Shared task submission due: December 19, 2025

We invite long and short papers on all topics related to fact extraction and verification, including:

  • Information Extraction
  • Semantic Parsing
  • Knowledge Base Population
  • Natural Language Inference
  • Textual Entailment Recognition
  • Argumentation Mining
  • Machine Reading and Comprehension
  • Claim Validation/Fact checking
  • Question Answering
  • Information Retrieval and Seeking
  • Theorem Proving
  • Stance detection
  • Adversarial learning
  • Computational journalism
  • Descriptions of systems for the FEVER, FEVER 2.0, FEVEROUS, AVERITEC and AVERITEC 2.0 Shared Tasks

Long/short papers should consist of eight/four pages of original content plus unlimited pages for bibliography. Submissions must be in PDF format, anonymized for review, and follow the EACL 2026 conference submission guidelines, using the LaTeX style files, Word templates or the Overleaf template from the official EACL website.

Each long paper submission consists of up to eight (8) pages of content, plus unlimited pages for references; final versions of long papers will be given one additional page (up to nine pages with unlimited pages for references) so that reviewers’ comments can be taken into account.

Each short paper submission consists of up to four (4) pages of content, plus unlimited pages for references; final versions of short papers will be given one additional page (up to five pages in the proceedings and unlimited pages for references) so that reviewers’ comments can be taken into account.

The review will be double-blind (two-way anonymized review). Please do not include any self-identifying information in the submission. Papers can be submitted as non-archival, so that their content can be reused for other venues. Please put a footnote stating "NON-ARCHIVAL submission" on the first page. Non-archival papers will follow the same submission guidelines, and, if accepted, will be linked from the FEVER website but not from the EACL proceedings. Previously published work can also be submitted in this manner, with the additional requirement to state on the first page the original publication. In this case, the paper does not need to be anonymized.

Limitations Section (mandatory): Following the EACL format, we require all papers to have a discussion of limitations, in a section titled “Limitations”. The section should appear at the end of the paper, after the conclusions section and before the references, and will not count towards the page limit.

Ethics Statement (optional, but highly recommended): We also highly recommend including an ethics statement. We allow extra space (hence not counting towards page limits) for a section at the end of the paper for a broader impact statement and other discussions of ethics.

Moreover, please review and abide by the ACL Ethics Policy as outlined below: "Authors are required to honor the ethical code set out in the ACL Code of Ethics. The consideration of the ethical impact of our research, use of data, and potential applications of our work has always been an important consideration, and as artificial intelligence is becoming more mainstream, these issues are increasingly pertinent. We ask that all authors read the code, and ensure that their work is conformant to this code. Where a paper may raise ethical issues, we ask that you include in the paper an explicit discussion of these issues, which will be taken into account in the review process. We reserve the right to reject papers on ethical grounds, where the authors are judged to have operated counter to the code of ethics, or have inadequately addressed legitimate ethical concerns with their work."

Important dates

All deadlines are 11.59 pm UTC-12h ("anywhere on Earth").

Workshop Organising Committee

Mubashara Akhtar

King's College London

Rami Aly

University of Cambridge

Rui Cao

University of Cambridge

Yulong Chen

University of Cambridge

Oana Cocarascu

King's College London

Zhenyun Deng

University of Cambridge

Zifeng Ding

University of Cambridge

Zhijiang Guo

HKUST (GZ)

Arpit Mittal

Meta

Michael Schlichtkrull

Queen Mary University of London

James Thorne

KAIST AI

Chenxi Whitehouse

Meta

Andreas Vlachos

University of Cambridge