Evolution of Thought: Diverse and High-Quality Reasoning via Multi-Objective Optimization

• URL: http://arxiv.org/abs/2412.07779v1
• Date: Sun, 24 Nov 2024 14:59:30 GMT
• Title: Evolution of Thought: Diverse and High-Quality Reasoning via Multi-Objective Optimization
• Authors: Biqing Qi, Zhouyi Qian, Yiang Luo, Junqi Gao, Dong Li, Kaiyan Zhang, Bowen Zhou,
• Abstract summary: Multi-modal large language models (MLLMs) are increasingly applied to complex reasoning tasks.<n>We propose Evolution of Thought (EoT) to foster both high-quality and diverse reasoning paths.<n>We show EoT achieves superior reasoning performance and efficiency compared to other competitive baselines.
• Score: 14.346638764967357
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: As multi-modal large language models (MLLMs) are increasingly applied to complex reasoning tasks, the diversity and quality of reasoning paths become crucial factors affecting their performance. Although current methods aim to enhance reasoning quality through path expansion, they often neglect the diversity of reasoning paths and effective information sharing, leading to local optima and inefficiency. To address these challenges, we propose Evolution of Thought (EoT), a multi-objective framework designed to improve reasoning by fostering both high-quality and diverse reasoning paths. Specifically, we introduce the Non-dominated Sorting Genetic Algorithm II for multi-objective optimization, utilizing crossover and mutation operators to promote greater diversity in reasoning solutions. Additionally, we propose a Condensation-Aggregation mechanism to cluster and eliminate redundant paths, facilitate improved information sharing among parent nodes, and ultimately enhance both the efficiency and quality of the reasoning process. Validation experiments on various vision-language and language reasoning tasks demonstrate that EoT achieves superior reasoning performance and efficiency compared to other competitive baselines. Our study provides a novel perspective on the design of heuristic reasoning frameworks for MLLMs.

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