title: Thread of Thought Unraveling Chaotic Contexts
summary: 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.
paper_authors._id: 6555a9db48093c3f6e4da4d4
paper_authors.name: Yucheng Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-16T10:21:28.005Z
paper_authors._id: 6388737ad444627f6d0085c3
paper_authors.avatarUrl: /avatars/680f371516b8a69882bc6425d229b2ca.svg
paper_authors.fullname: yucheng zhou
paper_authors.type: user
paper_authors.user: ystar
paper_authors._id: 6555a9db48093c3f6e4da4d5
paper_authors.name: Xiubo Geng
paper_authors._id: 6555a9db48093c3f6e4da4d6
paper_authors.name: Tao Shen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-16T10:22:35.016Z
paper_authors._id: 602331cdc1f3c79f98e4b378
paper_authors.avatarUrl: /avatars/fbcf4ac9059df87ee788dc431f46606a.svg
paper_authors.fullname: Tao Shen
paper_authors.type: user
paper_authors.user: taoshen58
paper_authors._id: 6555a9db48093c3f6e4da4d7
paper_authors.name: Chongyang Tao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-16T10:22:41.930Z
paper_authors._id: 648db8f92eb481bc8bc60469
paper_authors.avatarUrl: /avatars/7a640980ed225e31117f750e09bcc18c.svg
paper_authors.fullname: Chongyang Tao
paper_authors.type: user
paper_authors.user: chongyang09
paper_authors._id: 6555a9db48093c3f6e4da4d8
paper_authors.name: Guodong Long
paper_authors._id: 6555a9db48093c3f6e4da4d9
paper_authors.name: Jian-Guang Lou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-16T10:22:59.763Z
paper_authors._id: 6554338ba4de813482e91ad5
paper_authors.avatarUrl: /avatars/bee879fa4a818fd6c070ee001769ce05.svg
paper_authors.fullname: Jian-Guang Lou
paper_authors.type: user
paper_authors.user: substill
paper_authors._id: 6555a9db48093c3f6e4da4da
paper_authors.name: Jianbing Shen
paper_title: Thread of Thought Unraveling Chaotic Contexts
paper_summary: 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.
paper_ai_summary: The "Thread of Thought" strategy enhances LLM reasoning by segmenting and analyzing chaotic contexts, improving performance across various datasets.
paper_ai_keywords: Large Language Models LLMs Thread of Thought ThoT human cognitive processes extended contexts pertinent information prompting techniques PopQA EntityQ Multi-Turn Conversation Response dataset MTCR reasoning performance