Thread of Thought Unraveling Chaotic Contexts
This addresses a specific challenge in natural language processing for users of LLMs, but it appears incremental as it builds on existing prompting techniques.
The paper tackles the problem of large language models (LLLs) struggling with chaotic contexts, such as distractors, by introducing the 'Thread of Thought' (ThoT) strategy, which segments and analyzes contexts to select relevant information, resulting in significant improvements in reasoning performance on datasets like PopQA, EntityQ, and MTCR.
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.