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Large Language Models (LLMs) have ushered in a transformative era in thefield of natural language processing, excelling in tasks related to textcomprehension and generation. Nevertheless, they encounter difficulties whenconfronted with chaotic contexts (e.g., distractors rather than long irrelevantcontext), leading to the inadvertent omission of certain details within thechaotic context. In response to these challenges, we introduce the "Thread ofThought" (ThoT) strategy, which draws inspiration from human cognitiveprocesses. ThoT systematically segments and analyzes extended contexts whileadeptly selecting pertinent information. This strategy serves as a versatile"plug-and-play" module, seamlessly integrating with various LLMs and promptingtechniques. In the experiments, we utilize the PopQA and EntityQ datasets, aswell as a Multi-Turn Conversation Response dataset (MTCR) we collected, toillustrate that ThoT significantly improves reasoning performance compared toother prompting techniques.

Thread of Thought Unraveling Chaotic Contexts