通过构建思维树，让大模型多角度思考，提升复杂问题解决能力
MTMT: Consolidating Multiple Thinking Modes to Form a Thought Tree for Strengthening LLM
- 设计思维树结构，融合联想、反事实、分解等多重思考模式
- 在GPT-4o mini上测试，显著提升复杂任务的推理准确率
- 适合需要深度逻辑推理的应用场景，如数学证明与策略规划
大型语言模型在需要复杂逻辑推理和多步求解的任务中表现受限。为应对这一挑战，研究者采用精心设计的提示和流程图，模拟人类认知过程以增强模型性能，如链式思维方法。本文提出MTMT（多思考模式树），一种与大模型交互的新方法，用于构建思维树，模拟包括联想、反事实思考、任务分解和比较在内的多种高级认知过程。通过将原始复杂任务拆解为更简单的子问题，MTMT使大模型更容易求解，从而更有效地利用其内部隐含知识。我们使用GPT-4o mini作为基础模型，在不同参数配置下评估了MTMT的表现。结果表明，整合多种思考模式能显著增强大模型处理复杂任务的能力。
原文摘要 · Abstract (English)
Large language models (LLMs) have shown limitations in tasks requiring complex logical reasoning and multi-step problem-solving. To address these challenges, researchers have employed carefully designed prompts and flowcharts, simulating human cognitive processes to enhance LLM performance, such as the Chain of Thought approach. In this paper, we introduce MTMT (Multi-thinking Modes Tree), a novel method that interacts with LLMs to construct a thought tree, simulating various advanced cognitive processes, including but not limited to association, counterfactual thinking, task decomposition, and comparison. By breaking down the original complex task into simpler sub-questions, MTMT facilitates easier problem-solving for LLMs, enabling more effective utilization of the latent knowledge within LLMs. We evaluate the performance of MTMT under different parameter configurations, using GPT-4o mini as the base model. Our results demonstrate that integrating multiple modes of thinking significantly enhances the ability of LLMs to handle complex tasks.
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