  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.
