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| Main Authors: | , , , , |
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| Format: | Preprint |
| Udgivet: |
2026
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| Online adgang: | https://arxiv.org/abs/2602.11221 |
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| _version_ | 1866911469342294016 |
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| author | Cao, Rui Deng, Zhenyun Chen, Yulong Schlichtkrull, Michael Vlachos, Andreas |
| author_facet | Cao, Rui Deng, Zhenyun Chen, Yulong Schlichtkrull, Michael Vlachos, Andreas |
| contents | The Automatic Verification of Image-Text Claims (AVerImaTeC) shared task aims to advance system development for retrieving evidence and verifying real-world image-text claims. Participants were allowed to either employ external knowledge sources, such as web search engines, or leverage the curated knowledge store provided by the organizers. System performance was evaluated using the AVerImaTeC score, defined as a conditional verdict accuracy in which a verdict is considered correct only when the associated evidence score exceeds a predefined threshold. The shared task attracted 14 submissions during the development phase and 6 submissions during the testing phase. All participating systems in the testing phase outperformed the baseline provided. The winning team, HUMANE, achieved an AVerImaTeC score of 0.5455. This paper provides a detailed description of the shared task, presents the complete evaluation results, and discusses key insights and lessons learned. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11221 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Automatic Verification of Image-Text Claims (AVerImaTeC) Shared Task Cao, Rui Deng, Zhenyun Chen, Yulong Schlichtkrull, Michael Vlachos, Andreas Computation and Language The Automatic Verification of Image-Text Claims (AVerImaTeC) shared task aims to advance system development for retrieving evidence and verifying real-world image-text claims. Participants were allowed to either employ external knowledge sources, such as web search engines, or leverage the curated knowledge store provided by the organizers. System performance was evaluated using the AVerImaTeC score, defined as a conditional verdict accuracy in which a verdict is considered correct only when the associated evidence score exceeds a predefined threshold. The shared task attracted 14 submissions during the development phase and 6 submissions during the testing phase. All participating systems in the testing phase outperformed the baseline provided. The winning team, HUMANE, achieved an AVerImaTeC score of 0.5455. This paper provides a detailed description of the shared task, presents the complete evaluation results, and discusses key insights and lessons learned. |
| title | The Automatic Verification of Image-Text Claims (AVerImaTeC) Shared Task |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.11221 |