QG-CoC: Question-Guided Chain-of-Captions for Large Multimodal Models

• URL: http://arxiv.org/abs/2511.03206v1
• Date: Wed, 05 Nov 2025 05:49:48 GMT
• Title: QG-CoC: Question-Guided Chain-of-Captions for Large Multimodal Models
• Authors: Kuei-Chun Kao, Hsu Tzu-Yin, Yunqi Hong, Ruochen Wang, Cho-Jui Hsieh,
• Abstract summary: We investigate how current prompting methods perceive fine-grained visual details and process visual information when dealing with multiple images.<n>Inspired by the findings, we propose a new zero-shot prompting method, Question-Guided Chain-of-Captions (QG-CoC)<n>We evaluate our method on various open-source and closed-source MLLMs for multi-image and single-image benchmarks.
• Score: 50.51641024244313
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, Multimodal Large Language Models (MLLMs) encounter two key issues in multi-image contexts: (1) a lack of fine-grained perception across disparate images, and (2) a diminished capability to effectively reason over and synthesize information from multiple visual inputs. However, while various prompting methods aim to describe visual content, many existing studies focus primarily on single-image settings or specific, constrained scenarios. This leaves a critical gap in understanding and addressing how MLLMs tackle more general and complex multi-image reasoning tasks. Thus, we first extensively investigate how current prompting methods perceive fine-grained visual details and process visual information when dealing with multiple images. Our findings reveal that existing prompting methods fall short in attending to needed clues and seamlessly integrating perception and reasoning. Inspired by the findings, we propose a new zero-shot prompting method, Question-Guided Chain-of-Captions (QG-CoC), a generalized prompting approach that effectively handles problems with an arbitrary number of images. We evaluate our method on various open-source and closed-source MLLMs for multi-image and single-image benchmarks. Experimental results indicate that QG-CoC demonstrates competitive performance across tasks and exhibits robust improvements in the challenging scenarios where existing prompting methods fail.

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