arXiv:2606.26923v1 Announce Type: new Abstract: Vision-language models (VLMs) often produce hallucinated or inconsistent outputs, where text and images are not properly aligned. Addressing this issue requires not only detecting misalignment but also explaining the discrepancy and localizing its visual evidence. We introduce GAVEL (Grounded Caption Error Verification and Localization), a task that jointly addresses verification, explanation, and localization for image-text pairs. To support systematic evaluation, we also present a corresponding dataset and benchmark. We further train a supervis
The proliferation of vision-language models makes addressing their inherent hallucination and inconsistency issues a critical next step to enhance reliability and utility.
Improved reliability and explainability in VLMs will accelerate their adoption across various industries, impacting decision-making and automation in critical sectors.
The introduction of GAVEL provides a standardized framework and dataset for evaluating and improving the accuracy and explainability of vision-language models, moving beyond simple error detection to practical error localization and explanation.
- · AI developers
- · Vision-language model users
- · Industries relying on VLM for analysis
- · Developers of unreliable VLMs
- · Manual data verification processes
VLMs become more trustworthy and are deployed in more sensitive applications.
Reduced need for human oversight in certain VLM-driven processes, leading to cost savings and faster operations.
Enhanced trust in AI systems could accelerate the development and adoption of AI agents in complex decision-making roles.
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Read at arXiv cs.CL