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

作     者：Qi, Biqing Qian, Zhouyi Luo, Yiang Gao, Junqi Li, Dong Zhang, Kaiyan Zhou, Bowen 

作者机构：Shanghai Artificial Intelligence Laboratory China School of Mathematics Harbin Institute of Technology China Department of Control Science and Engineering Harbin Institute of Technology China Department of Electronic Engineering Tsinghua University China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Visual languages 

摘      要：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. © 2024, CC BY-SA.