在大型语言模型中释放认知协同：通过多人格自我协作实现任务解决代理

Unleashing Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration

July 11, 2023

作者: Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji

cs.AI

摘要

人类智能源于认知协同的概念，即不同认知过程之间的协作和信息整合产生比独立认知过程更优越的结果。尽管大型语言模型（LLMs）已经展示出作为通用任务解决代理的有希望的性能，但它们仍然在需要深入领域知识和复杂推理的任务中遇到困难。在这项工作中，我们提出了独奏表现提示（SPP），通过与多个人物进行多轮自我协作，将单个LLM转化为认知协同者。认知协同者指的是与多个思维合作，结合其个体优势和知识以增强解决问题和复杂任务整体表现的智能代理。通过根据任务输入动态识别和模拟不同人物，SPP释放了LLMs中认知协同的潜力。我们发现，在LLMs中分配多个细粒度人物比使用单个或固定数量的人物能更好地激发解决问题的能力。我们在三个具有挑战性的任务上评估了SPP：知识创作题、密码合作和逻辑格子谜题，涵盖了知识密集型和推理密集型任务。与仅增强LLMs推理能力的先前作品（如Chain-of-Thought）不同，SPP有效地激发了内部知识获取能力，减少了幻觉，并保持了强大的推理能力。代码、数据和提示可在以下网址找到：https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git。

English

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.