Thread of Thought Unraveling Chaotic Contexts
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.
6555a9db48093c3f6e4da4d4
Yucheng Zhou
admin_assigned
2023-11-16T10:21:28.005Z
6388737ad444627f6d0085c3
/avatars/680f371516b8a69882bc6425d229b2ca.svg
yucheng zhou
user
ystar
6555a9db48093c3f6e4da4d5
Xiubo Geng
6555a9db48093c3f6e4da4d6
Tao Shen
admin_assigned
2023-11-16T10:22:35.016Z
602331cdc1f3c79f98e4b378
/avatars/fbcf4ac9059df87ee788dc431f46606a.svg
Tao Shen
user
taoshen58
6555a9db48093c3f6e4da4d7
Chongyang Tao
admin_assigned
2023-11-16T10:22:41.930Z
648db8f92eb481bc8bc60469
/avatars/7a640980ed225e31117f750e09bcc18c.svg
Chongyang Tao
user
chongyang09
6555a9db48093c3f6e4da4d8
Guodong Long
6555a9db48093c3f6e4da4d9
Jian-Guang Lou
admin_assigned
2023-11-16T10:22:59.763Z
6554338ba4de813482e91ad5
/avatars/bee879fa4a818fd6c070ee001769ce05.svg
Jian-Guang Lou
user
substill
6555a9db48093c3f6e4da4da
Jianbing Shen
Thread of Thought Unraveling Chaotic Contexts
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.
The "Thread of Thought" strategy enhances LLM reasoning by segmenting and analyzing chaotic contexts, improving performance across various datasets.
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