Unleashing Cognitive Synergy in Large Language Models: A Task-Solving
  Agent through Multi-Persona Self-Collaboration
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.
64adfc5b3c80c402fbe28c80
Zhenhailong Wang
claimed_verified
2023-07-12T13:28:51.828Z
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/avatars/dff2a3dd10d84b4a73fa486402de7219.svg
Zhenhailong Wang
user
mikewang
64adfc5b3c80c402fbe28c81
Shaoguang Mao
claimed_verified
2023-07-13T08:32:51.441Z
64af8d4b2cda6a37a4927d72
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/o37G6VkKSyP4N9xktFErV.png
Shaoguang Mao
user
dawnmsg
64adfc5b3c80c402fbe28c82
Wenshan Wu
64adfc5b3c80c402fbe28c83
Tao Ge
claimed_verified
2024-02-05T10:50:50.299Z
64ab5db78a1a9187c215f45a
/avatars/17add4cd02e183f9b9d4feb7b6c27c21.svg
Tao Ge
user
sggetao
64adfc5b3c80c402fbe28c84
Furu Wei
admin_assigned
2023-07-13T10:09:01.464Z
6368c512fbfe97c16a40baba
/avatars/1c23bc7c0b6d9225699ce27647623d7a.svg
Furu Wei
user
thegenerality
64adfc5b3c80c402fbe28c85
Heng Ji
Unleashing Cognitive Synergy in Large Language Models: A Task-Solving
  Agent through Multi-Persona Self-Collaboration
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.
Solo Performance Prompting (SPP) improves LLM performance by enabling internal persona collaboration, enhancing knowledge acquisition, reducing hallucination, and maintaining reasoning capabilities in complex tasks.
Large Language Models (LLMs) cognitive synergist multi-turn self-collaboration personas cognitive synergy Chain-of-Thought