title: Unleashing Cognitive Synergy in Large Language Models: A Task-Solving
    Agent through Multi-Persona Self-Collaboration
summary: Human intelligence thrives on the concept of cognitive synergy, where
  collaboration and information integration among different cognitive processes
  yield superior outcomes compared to individual cognitive processes in
  isolation. Although Large Language Models (LLMs) have demonstrated promising
  performance as general task-solving agents, they still struggle with tasks that
  require intensive domain knowledge and complex reasoning. In this work, we
  propose Solo Performance Prompting (SPP), which transforms a single LLM into a
  cognitive synergist by engaging in multi-turn self-collaboration with multiple
  personas. A cognitive synergist refers to an intelligent agent that
  collaborates with multiple minds, combining their individual strengths and
  knowledge, to enhance problem-solving and overall performance in complex tasks.
  By dynamically identifying and simulating different personas based on task
  inputs, SPP unleashes the potential of cognitive synergy in LLMs. We have
  discovered that assigning multiple, fine-grained personas in LLMs elicits
  better problem-solving abilities compared to using a single or fixed number of
  personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
  Codenames Collaborative, and Logic Grid Puzzle, encompassing both
  knowledge-intensive and reasoning-intensive types. Unlike previous works, such
  as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs, SPP
  effectively elicits internal knowledge acquisition abilities, reduces
  hallucination, and maintains strong reasoning capabilities. Code, data, and
  prompts can be found at:
  https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git.
paper_authors._id: 64adfc5b3c80c402fbe28c80
paper_authors.name: Zhenhailong Wang
paper_authors.status: claimed_verified
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paper_authors.fullname: Zhenhailong Wang
paper_authors.type: user
paper_authors.user: mikewang
paper_authors._id: 64adfc5b3c80c402fbe28c81
paper_authors.name: Shaoguang Mao
paper_authors.status: claimed_verified
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paper_authors.fullname: Shaoguang Mao
paper_authors.type: user
paper_authors.user: dawnmsg
paper_authors._id: 64adfc5b3c80c402fbe28c82
paper_authors.name: Wenshan Wu
paper_authors._id: 64adfc5b3c80c402fbe28c83
paper_authors.name: Tao Ge
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-05T10:50:50.299Z
paper_authors._id: 64ab5db78a1a9187c215f45a
paper_authors.avatarUrl: /avatars/17add4cd02e183f9b9d4feb7b6c27c21.svg
paper_authors.fullname: Tao Ge
paper_authors.type: user
paper_authors.user: sggetao
paper_authors._id: 64adfc5b3c80c402fbe28c84
paper_authors.name: Furu Wei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-13T10:09:01.464Z
paper_authors._id: 6368c512fbfe97c16a40baba
paper_authors.avatarUrl: /avatars/1c23bc7c0b6d9225699ce27647623d7a.svg
paper_authors.fullname: Furu Wei
paper_authors.type: user
paper_authors.user: thegenerality
paper_authors._id: 64adfc5b3c80c402fbe28c85
paper_authors.name: Heng Ji
paper_title: Unleashing Cognitive Synergy in Large Language Models: A Task-Solving
    Agent through Multi-Persona Self-Collaboration
paper_summary: Human intelligence thrives on the concept of cognitive synergy, where
  collaboration and information integration among different cognitive processes
  yield superior outcomes compared to individual cognitive processes in
  isolation. Although Large Language Models (LLMs) have demonstrated promising
  performance as general task-solving agents, they still struggle with tasks that
  require intensive domain knowledge and complex reasoning. In this work, we
  propose Solo Performance Prompting (SPP), which transforms a single LLM into a
  cognitive synergist by engaging in multi-turn self-collaboration with multiple
  personas. A cognitive synergist refers to an intelligent agent that
  collaborates with multiple minds, combining their individual strengths and
  knowledge, to enhance problem-solving and overall performance in complex tasks.
  By dynamically identifying and simulating different personas based on task
  inputs, SPP unleashes the potential of cognitive synergy in LLMs. We have
  discovered that assigning multiple, fine-grained personas in LLMs elicits
  better problem-solving abilities compared to using a single or fixed number of
  personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
  Codenames Collaborative, and Logic Grid Puzzle, encompassing both
  knowledge-intensive and reasoning-intensive types. Unlike previous works, such
  as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs, SPP
  effectively elicits internal knowledge acquisition abilities, reduces
  hallucination, and maintains strong reasoning capabilities. Code, data, and
  prompts can be found at:
  https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git.
paper_ai_summary: Solo Performance Prompting (SPP) improves LLM performance by enabling internal persona collaboration, enhancing knowledge acquisition, reducing hallucination, and maintaining reasoning capabilities in complex tasks.
paper_ai_keywords: Large Language Models (LLMs) cognitive synergist multi-turn self-collaboration personas cognitive synergy Chain-of-Thought