用哲学辩证法让大模型自我反思，提升推理与创新力。
Self-reflecting Large Language Models: A Hegelian Dialectical Approach
- 将黑格尔辩证法形式化为迭代优化过程，生成对立观点并融合。
- 在GSM-8k等数据集上显著超越基线，开放科学创意质量高。
- 适合研究推理增强与跨领域创新的AI学者使用。
本文提出一种基于黑格尔辩证法的自反思框架，将初始命题与生成的对立观点通过迭代融合形成更全面的思想。该方法应用于两类任务：跨学科（数学、物理、经济、哲学）生成新科学思想，以及通过结构化自批判提升大模型的推理能力。研究对比了动态温度退火（从探索到精炼）与恒定温度两种配置，旨在分析温度策略的影响而非推崇其一。为无专家评估思想有效性，引入多智能体多数投票机制（MAMV），由多个大模型独立判断合成结果的合理性和新颖性。实验表明，在数学（GSM-8k, GSM-hard）、符号推理（GSM-Symbolic）及知识密集型任务（MMLU Pro）上均显著优于基线，开放式科学创意也展现出良好质素。
原文摘要 · Abstract (English)
In this paper, we introduce a self-reflection framework for Large Language Models (LLMs) grounded in the Hegelian Dialectic, a philosophical method in which an initial proposition is challenged by a generated opposition, and both are reconciled into a unified, more comprehensive idea. We formalize this process as an iterative operator over the space of consistent theories and apply it to two complementary tasks:(i) generating novel scientific ideas across domains such as mathematics, physics, economics, and philosophy, and (ii)improving reasoning by enabling LLMs to identify and correct their own errors through structured self-critique. We study generation temperature through two configurations (a dynamic annealing schedule that shifts from creative exploration to refinement, and a constant temperature), to examine the effect of fixed versus dynamic temperature rather than advocate either. To evaluate ideas without domain experts, we introduce Multi-Agent Majority Voting (MAMV), in which multiple LLMs independently assess the validity and novelty of each synthesis. Our experiments show significant gains over baselines on mathematical (GSM-8k, GSM-hard), symbolic (GSM-Symbolic), and knowledge-intensive (MMLU Pro) reasoning, with promising qualitative results in open-ended scientific ideation.
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