Large Language Models (LLMs) have ushered in a transformative era in the field of natural language processing, excelling in tasks related to text comprehension and generation. Nevertheless, they encounter difficulties when confronted with chaotic contexts (e.g., distractors rather than long irrelevant context), leading to the inadvertent omission of certain details within the chaotic context. In response to these challenges, we introduce the "Thread of Thought" (ThoT) strategy, which draws inspiration from human cognitive processes. ThoT systematically segments and analyzes extended contexts while adeptly selecting pertinent information. This strategy serves as a versatile "plug-and-play" module, seamlessly integrating with various LLMs and prompting techniques. In the experiments, we utilize the PopQA and EntityQ datasets, as well as a Multi-Turn Conversation Response dataset (MTCR) we collected, to illustrate that ThoT significantly improves reasoning performance compared to other prompting techniques.

翻译：大语言模型（LLMs）已在自然语言处理领域开启变革时代，在文本理解与生成任务中表现卓越。然而，面对混乱语境（如干扰项而非长无关上下文），它们会遭遇困难，导致无意中遗漏混乱语境中的某些细节。为应对这些挑战，我们提出"思考之线"（Thread of Thought, ThoT）策略，该策略受人类认知过程启发。ThoT系统性地分割并分析扩展语境，同时巧妙选择相关信息。该策略作为通用的"即插即用"模块，可与多种LLMs及提示技术无缝集成。实验中，我们利用PopQA和EntityQ数据集，以及自收集的多轮对话响应数据集（MTCR），论证了ThoT相较于其他提示技术能显著提升推理性能。