MTMT: Consolidating Multiple Thinking Modes to Form a Thought Tree for Strengthening LLM

TL;DR

This paper introduces MTMT, a method that constructs a thought tree by integrating multiple thinking modes to improve LLMs' performance on complex reasoning tasks, surpassing traditional prompt techniques.

Contribution

The paper presents a novel multi-thinking modes tree approach that simulates diverse cognitive processes to enhance LLM reasoning capabilities.

Findings

MTMT significantly improves LLM performance on complex tasks.

Integrating multiple thinking modes outperforms single-mode approaches.

Evaluation with GPT-4o mini shows notable performance gains.

Abstract

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.