arXiv:2606.31407v1 Announce Type: cross Abstract: Vision-language models can produce confident answers on visually ambiguous inputs, resulting in biased predictions. Common entropy-based methods, such as Semantic Entropy (SE), rely on output diversity. Yet our analysis shows that overconfident visual embeddings suppress output diversity under stochastic decoding, causing SE to underestimate uncertainty in such cases. Recent methods instead probe output diversity through input perturbations, including textual paraphrasing or joint text-image perturbations, and show improved performance. We stud
The rapid deployment of Vision Language Models (VLMs) across various applications necessitates robust methods for evaluating their reliability, particularly concerning uncertainty in visual interpretation.
Understanding and addressing visual ambiguity in VLMs is critical for their safe and effective deployment, especially in high-stakes environments where misinterpretation can lead to significant errors.
This research highlights limitations in current uncertainty estimation for VLMs and proposes new avenues for improving confidence calibration, moving beyond simple entropy measures.
- · AI Safety Researchers
- · Developers of robust VLMs
- · Industries relying on VLM accuracy
- · Applications of VLMs in critical domains without proper uncertainty handling
- · Methods relying solely on basic entropy for VLM uncertainty
Improved methods for evaluating and enhancing the robustness of Vision Language Models.
Increased trust and wider adoption of VLMs in sensitive applications requiring high reliability.
The development of a new generation of VLMs inherently designed with sophisticated visual ambiguity recognition capabilities.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL