Enhancing Video-LLM Reasoning via Agent-of-Thoughts Distillation

• URL: http://arxiv.org/abs/2412.01694v2
• Date: Thu, 13 Feb 2025 07:42:33 GMT
• Title: Enhancing Video-LLM Reasoning via Agent-of-Thoughts Distillation
• Authors: Yudi Shi, Shangzhe Di, Qirui Chen, Weidi Xie,
• Abstract summary: This paper tackles the problem of video question answering (VideoQA)<n>Large video-language models perform well on benchmarks, but they often lack explainability and spatial-temporal grounding.<n>We propose Agent-of-Thoughts Distillation (AoTD), a method that enhances models by incorporating automatically generated Chain-of-Thoughts (CoTs) into the instruction-tuning process.
• Score: 32.930999188946345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper tackles the problem of video question answering (VideoQA), a task that often requires multi-step reasoning and a profound understanding of spatial-temporal dynamics. While large video-language models perform well on benchmarks, they often lack explainability and spatial-temporal grounding. In this paper, we propose Agent-of-Thoughts Distillation (AoTD), a method that enhances models by incorporating automatically generated Chain-of-Thoughts (CoTs) into the instruction-tuning process. Specifically, we leverage an agent-based system to decompose complex questions into sub-tasks, and address them with specialized vision models, the intermediate results are then treated as reasoning chains. We also introduce a verification mechanism using a large language model (LLM) to ensure the reliability of generated CoTs. Extensive experiments demonstrate that AoTD improves the performance on multiple-choice and open-ended benchmarks.

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