OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities
Yuanzhen Xie Tao Xie Mingxiong Lin WenTao Wei Chenglin Li Beibei Kong Lei Chen Chengxiang Zhuo Bo Hu Zang Li
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: \url{https://github.com/oladata-team/OlaGPT}.