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Arxiv
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Rationale-Enhanced Decoding for Multi-modal Chain-of-Thought
- Large vision-language models (LVLMs) integrating vision encoders with language models use chain-of-thought (CoT) prompting for multi-modal reasoning.
- Existing LVLMs struggle with incorporating the contents of generated rationales in CoT reasoning, impacting grounding and accuracy.
- Researchers propose rationale-enhanced decoding (RED) as an inference-time strategy for improved multi-modal CoT reasoning.
- Extensive experiments show RED significantly enhances reasoning over standard CoT and other decoding methods in LVLMs, improving faithfulness and accuracy.
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