增强推理的多模态链式推理
Rationale-Enhanced Decoding for Multi-modal Chain-of-Thought
AI总结：
本文提出RED方法，通过KL约束奖励最大化提升多模态链式推理的准确性和忠实度，实验表明其在多个基准和模型上显著优于传统方法。
摘要：
大型视觉-语言模型（LVLMs）通过整合预训练视觉编码器和大语言模型（LLMs）展现了卓越能力。类似单模态LLMs，链式推理（CoT）提示已被适应用于LVLMs以增强多模态推理，通过生成基于视觉和文本输入的中间推理。尽管CoT被认为能提升LVLMs的接地性和准确性，我们的实验揭示了关键挑战：现有LVLMs常忽略生成推理中的内容。为此，我们将多模态CoT推理重新表述为KL约束奖励最大化，聚焦于推理条件对数似然。作为最优解，我们提出推理增强解码（RED），一种新颖的即插即用推理时解码策略。RED通过乘以不同的图像条件和推理条件下一个令牌分布和谐统一视觉和推理信息。大量实验表明，RED在多个基准和LVLMs上一致且显著提升了推理性能。本文提供了一种实用有效的改进多模态系统推理忠实度和准确度的方法，为更可靠的推理基础多模态系统铺平道路。代码可在https://github.com/yshinya6/red/获取。
英文摘要：
Large vision-language models (LVLMs) have demonstrated remarkable capabilities by integrating pre-trained vision encoders with large language models (LLMs). Similar to single-modal LLMs, chain-of-thought (CoT) prompting has been adapted for LVLMs to enhance multi-modal reasoning by generating intermediate rationales based on visual and textual inputs. While CoT is assumed to improve grounding and accuracy in LVLMs, our experiments reveal a key challenge: existing LVLMs often ignore the contents of generated rationales in CoT reasoning. To address this, we re-formulate multi-modal CoT reasoning as a KL-constrained reward maximization focused on rationale-conditional log-likelihood. As the optimal solution, we propose rationale-enhanced decoding (RED), a novel plug-and-play inference-time decoding strategy. RED harmonizes visual and rationale information by multiplying distinct image-conditional and rationale-conditional next token distributions. Extensive experiments show that RED consistently and significantly improves reasoning over standard CoT and other decoding methods across multiple benchmarks and LVLMs. Our work offers a practical and effective approach to improve both the faithfulness and accuracy of CoT reasoning in LVLMs, paving the way for more reliable rationale-grounded multi-modal systems. Code is available at https://github.com/yshinya6/red/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
