Evaluating Explanation-Driven Vision-Language Reasoning via Generation Order Interventions
Natural language explanation generation serves as a key mechanism for exposing and evaluating vision-language reasoning. Prior work on explanation-driven vision-language models predominantly follows a post-hoc (answer-first) paradigm, implicitly suggesting that supervised rationales can reflect underlying reasoning processes. In contrast, modern large vision-language models increasingly exhibit a rationale-first generation tendency, which more closely aligns with structured, stepwise reasoning. In this work, we systematically evaluate whether explanations are causally tied to model predictions within a single generation step under a controlled experimental setup, explicitly eliminating un...
| Authors | Siting Liang, Luca Rippe, Omar Adjali et al. |
|---|---|
| Primary category | cs.CL |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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