MTMT: Consolidating Multiple Thinking Modes to Form a Thought Tree for Strengthening LLM
This addresses the problem of improving LLM reasoning for tasks requiring multi-step problem-solving, though it appears incremental as it builds on existing prompt-based approaches like Chain of Thought.
The paper tackles the limitations of large language models in complex logical reasoning by introducing MTMT, a method that constructs a thought tree using multiple cognitive modes like association and task decomposition, and shows that it significantly enhances LLM performance on complex tasks when evaluated with GPT-4o mini.
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.