LookBack: Where and How to Score LVLM Responses via Visual Reference Usage
Large Vision-Language Models (LVLMs) integrate visual perception with language generation, enabling responses that span image understanding and complex reasoning. However, LVLMs do not just inherit the text-level hallucinations; they also hallucinate against the image, producing fluent responses ungrounded in what they see. This makes LVLM response scoring inherently harder, and our diagnostics show that existing confidence-based metrics adopted from LLMs are insufficient for LVLMs. Specifically, removing the input image barely changes confidence-based selection, suggesting that output-space confidence primarily captures textual plausibility rather than agreement with the image. To addres...
| Authors | Beomsik Cho, Jinhyeong Kim, Dongseok Lee et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 August 2026 |
| Last updated at source | 31 August 2026 |
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