Chain-of-Description: What I can understand, I can put into words
Jiaxin Guo, Daimeng Wei, Zongyao Li, Hengchao Shang, Yuanchang Luo,, Hao Yang

TL;DR
This paper introduces Chain-of-Description Prompting, a new method for Multi-Modal Large Language Models that improves performance by encouraging detailed input descriptions before answering, validated on audio and vision benchmarks.
Contribution
The paper proposes Chain-of-Description Prompting, a novel strategy that enhances multi-modal model performance by structured input description, demonstrating significant improvements over standard prompts.
Findings
Nearly 4% improvement on AIR-Bench-Chat audio benchmark
5.3% improvement on MMMU_Pro vision benchmark
Ablation study confirms effectiveness of CoD Prompting
Abstract
In this paper, we propose a novel strategy defined as Chain-of-Description (CoD) Prompting, tailored for Multi-Modal Large Language Models. This approach involves having the model first provide a detailed description of the multi-modal input before generating an answer to the question. When applied to models such as Qwen2-Audio, Qwen2-VL, and Qwen2.5-VL, CoD Prompting significantly enhances performance compared to standard prompting methods. This is demonstrated by nearly a 4\% improvement in the speech category of the audio benchmark AIR-Bench-Chat and a 5.3\% improvement in the hard-level portion of the vision benchmark MMMU\_Pro. Our ablation study further validates the effectiveness of CoD Prompting.
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Taxonomy
TopicsDigital Humanities and Scholarship · Semantic Web and Ontologies
