title: OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities
summary: In most current research, large language models (LLMs) are able to perform
  reasoning tasks by generating chains of thought through the guidance of
  specific prompts. However, there still exists a significant discrepancy between
  their capability in solving complex reasoning problems and that of humans. At
  present, most approaches focus on chains of thought (COT) and tool use, without
  considering the adoption and application of human cognitive frameworks. It is
  well-known that when confronting complex reasoning challenges, humans typically
  employ various cognitive abilities, and necessitate interaction with all
  aspects of tools, knowledge, and the external environment information to
  accomplish intricate tasks. This paper introduces a novel intelligent
  framework, referred to as OlaGPT. OlaGPT carefully studied a cognitive
  architecture framework, and propose to simulate certain aspects of human
  cognition. The framework involves approximating different cognitive modules,
  including attention, memory, reasoning, learning, and corresponding scheduling
  and decision-making mechanisms. Inspired by the active learning mechanism of
  human beings, it proposes a learning unit to record previous mistakes and
  expert opinions, and dynamically refer to them to strengthen their ability to
  solve similar problems. The paper also outlines common effective reasoning
  frameworks for human problem-solving and designs Chain-of-Thought (COT)
  templates accordingly. A comprehensive decision-making mechanism is also
  proposed to maximize model accuracy. The efficacy of OlaGPT has been
  stringently evaluated on multiple reasoning datasets, and the experimental
  outcomes reveal that OlaGPT surpasses state-of-the-art benchmarks,
  demonstrating its superior performance. Our implementation of OlaGPT is
  available on GitHub: https://github.com/oladata-team/OlaGPT.
paper_authors._id: 64743d4ba7f23affe77975ae
paper_authors.name: Yuanzhen Xie
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-30T10:07:50.675Z
paper_authors._id: 6475c5804a0bae093fe228dc
paper_authors.avatarUrl: /avatars/3392516a9af1a96e632d8040b49fd42c.svg
paper_authors.fullname: Yuanzhen Xie
paper_authors.type: user
paper_authors.user: xieyzh3
paper_authors._id: 64743d4ba7f23affe77975af
paper_authors.name: Tao Xie
paper_authors._id: 64743d4ba7f23affe77975b0
paper_authors.name: Mingxiong Lin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-30T11:11:28.615Z
paper_authors._id: 6475c5a54766357252e6b29c
paper_authors.avatarUrl: /avatars/83d7e994ac3aea5bc3138badd4212401.svg
paper_authors.fullname: myxiaoyu
paper_authors.type: user
paper_authors.user: myxiaoyu
paper_authors._id: 64743d4ba7f23affe77975b1
paper_authors.name: WenTao Wei
paper_authors._id: 64743d4ba7f23affe77975b2
paper_authors.name: Chenglin Li
paper_authors._id: 64743d4ba7f23affe77975b3
paper_authors.name: Beibei Kong
paper_authors._id: 64743d4ba7f23affe77975b4
paper_authors.name: Lei Chen
paper_authors._id: 64743d4ba7f23affe77975b5
paper_authors.name: Chengxiang Zhuo
paper_authors._id: 64743d4ba7f23affe77975b6
paper_authors.name: Bo Hu
paper_authors._id: 64743d4ba7f23affe77975b7
paper_authors.name: Zang Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-30T07:46:36.295Z
paper_authors._id: 646754c5946476c5d2159ffa
paper_authors.avatarUrl: /avatars/dc01330a3620862c4e1ebe79ae06737c.svg
paper_authors.fullname: Zang Li
paper_authors.type: user
paper_authors.user: tasion2011
paper_title: OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities
paper_summary: In most current research, large language models (LLMs) are able to perform
  reasoning tasks by generating chains of thought through the guidance of
  specific prompts. However, there still exists a significant discrepancy between
  their capability in solving complex reasoning problems and that of humans. At
  present, most approaches focus on chains of thought (COT) and tool use, without
  considering the adoption and application of human cognitive frameworks. It is
  well-known that when confronting complex reasoning challenges, humans typically
  employ various cognitive abilities, and necessitate interaction with all
  aspects of tools, knowledge, and the external environment information to
  accomplish intricate tasks. This paper introduces a novel intelligent
  framework, referred to as OlaGPT. OlaGPT carefully studied a cognitive
  architecture framework, and propose to simulate certain aspects of human
  cognition. The framework involves approximating different cognitive modules,
  including attention, memory, reasoning, learning, and corresponding scheduling
  and decision-making mechanisms. Inspired by the active learning mechanism of
  human beings, it proposes a learning unit to record previous mistakes and
  expert opinions, and dynamically refer to them to strengthen their ability to
  solve similar problems. The paper also outlines common effective reasoning
  frameworks for human problem-solving and designs Chain-of-Thought (COT)
  templates accordingly. A comprehensive decision-making mechanism is also
  proposed to maximize model accuracy. The efficacy of OlaGPT has been
  stringently evaluated on multiple reasoning datasets, and the experimental
  outcomes reveal that OlaGPT surpasses state-of-the-art benchmarks,
  demonstrating its superior performance. Our implementation of OlaGPT is
  available on GitHub: https://github.com/oladata-team/OlaGPT.
paper_ai_summary: OlaGPT is a cognitive architecture framework that simulates human cognitive modules to improve reasoning capabilities in large language models.
paper_ai_keywords: chains of thought tool use cognitive architecture attention memory reasoning learning scheduling decision-making active learning Chain-of-Thought templates