MoT: Pre-thinking and Recalling Enable ChatGPT to Self-Improve with Memory-of-Thoughts

Large Language Models have shown impressive abilities on various tasks. However, fundamentally improving them depends on high-quality datasets or computationally expensive fine-tuning. On the contrary, human can easily improve themselves by thinking and memory, without external resources. In this paper, we propose a framework, MoT, to let the LLM self-improve through Memory of Thoughts, without annotated datasets and parameter updates. Specifically, the framework is divided into two stages: 1. before the test stage, we let the LLM pre-think on the unlabeled dataset and save the high-confidence thoughts as external memory; 2. during inference, given a test question, we let the LLM recall relevant memory to help itself reason and answer it. Experimental results show that the proposed framework can help ChatGPT significantly improve its abilities in math reasoning, commonsense reasoning, factual reasoning and natural language inference. Further analyses show that each component contributes critically to the improvements.