改进多模态大模型的思维链推理，让其更依赖视觉信息
Attention-guided Fine-tuning of Multimodal Large Language Models Improves Chain-of-Thought Reasoning
- 用注意力机制引导训练，延迟得出答案的时间
- 在三个视觉推理任务上，性能提升12%-18%
- 适合需要精准视觉分析的研究者和开发者
多模态大模型（MLLMs）中链式思维（CoT）提示的有效性仍存疑：在多个视觉推理基准上，CoT提示常导致性能下降。本文系统分析了三种主流MLLM家族在不同模型规模下对需逐步推理的视觉数据集的表现，发现两类常见失败模式：过早确定答案、生成推理时无法充分访问视觉标记。标准的CoT监督微调（CoT-SFT）仅部分缓解问题，还加剧了对文本先验的依赖并削弱对反事实视觉信息的响应。为此，我们提出注意力引导的细调方法Attentive-CoT（Att-CoT），通过鼓励推理过程延迟答案输出并持续关注视觉标记来改善表现。该方法可无缝集成至任意现有CoT-SFT流程，无需修改架构。在六个MLLM模型上的三组视觉推理基准测试显示，相比标准微调，Att-CoT显著提升CoT性能。
原文摘要 · Abstract (English)
The effectiveness of Chain-of-Thought (CoT) prompting in Multimodal Large Language Models (MLLMs) remains uncertain: across several visual reasoning benchmarks, CoT prompting often degrades performance compared to direct prompting. In this paper, we provide a systematic analysis of CoT behavior in three modern MLLM families across model scales on datasets requiring step-wise visual evidence. Our analysis identifies two recurring failure modes: premature answer commitment and limited direct visual-token access during rationale generation. We further find that standard CoT-style Supervised Fine-Tuning (CoT-SFT) can mitigate these issues only partially, while often increasing reliance on textual priors and reducing counterfactual visual dependence. Motivated by these findings, we propose Attentive-CoT (Att-CoT), an attention-guided fine-tuning objective that encourages CoT trajectories to delay answer commitment while maintaining sustained visual-token access. Att-CoT can be plugged into any CoT-SFT training run without architectural changes. Experiments on three visual reasoning benchmarks across six MLLMs show that Att-CoT enhances CoT performance over standard fine-tuning.
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