MTMT：整合多种思维模式以形成思维树来增强 LLM
MTMT: Consolidating Multiple Thinking Modes to Form a Thought Tree for Strengthening LLM
AI总结：
针对 LLM 复杂推理不足的问题，本文提出 MTMT，通过整合联想、反事实、分解和比较等多种思维模式构建思维树，并在 GPT-4o mini 上验证其可显著提升复杂任务处理能力。
摘要：
大型语言模型（LLM）在需要复杂逻辑推理和多步问题解决的任务中表现出局限性。为应对这些挑战，研究人员采用精心设计的提示词和流程图，模拟人类认知过程以提升 LLM 的性能，例如 Chain of Thought（思维链）方法。本文提出 MTMT（Multi-thinking Modes Tree，多思维模式树），这是一种与 LLM 交互以构建思维树的新方法，可模拟多种高级认知过程，包括但不限于联想、反事实思维、任务分解和比较。通过将原始复杂任务分解为更简单的子问题，MTMT 使 LLM 更容易解决问题，从而能够更有效地利用 LLM 内部的潜在知识。我们使用 GPT-4o mini 作为基础模型，评估了 MTMT 在不同参数配置下的性能。结果表明，整合多种思维模式显著增强了 LLM 处理复杂任务的能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
