View Proposal

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Proposer

Gavin Abercrombie

Title

Automated Fact Verification

Goal

To evaluate the ability of systems to verify real-world image-text claims with evidence from the Web.

Description

'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' [1]. This project will tackle the AVerImaTeC shared task [2]: The AVerImaTeC challenge aims to evaluate the ability of systems to verify real-world image-text claims with evidence from the Web. - Given an image-text claim and its metadata, the systems must retrieve evidence (consisting of text and/or images) that supports and/or refutes the claim, either from the Web or from the document and image evidence collection provided by the organisers.. - According to the evidence principle in fact-checking, all evidence should be published before the claim date. - Using this evidence, label the claim as Supported, Refuted given the evidence, Not Enough Evidence (if there isn't sufficient evidence to either support or refute it) or Conflicting Evidence/Cherry-picking (if the claim has both supporting and refuting evidence). - A response will be considered correct only if both the label is correct and the evidence adequate. As evidence retrieval evaluation is non-trivial to perform automatically, the participants will be asked to help evaluate it manually to assess the systems fairly.

Resources

[1] https://fever.ai/workshop.html [2] Cao, Rui and Ding, Zifeng and Guo, Zhijiang and Schlichtkrull, Michael and Vlachos, Andreas. 2025. AVerImaTeC: A Dataset for Automatic Verification of Image-Text Claims with Evidence from the Web

Background

Url

External Link

Difficulty Level

Moderate

Ethical Approval

Full

Number Of Students

1

Supervisor

Gavin Abercrombie

Keywords

nlp, image-text models

Degrees

Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence with SMI
Master of Science in Data Science