OlaGPT：赋予LLM人类般的问题解决能力

摘要

在当前大多数研究中，大型语言模型（LLMs）能够通过特定提示的引导生成思维链来执行推理任务。然而，它们在解决复杂推理问题的能力与人类之间仍存在显著差距。目前，大多数方法侧重于思维链（COT）和工具使用，而未考虑采用和应用人类认知框架。众所周知，面对复杂推理挑战时，人类通常运用各种认知能力，并需要与工具、知识和外部环境信息的各个方面进行互动，以完成复杂任务。本文介绍了一种新颖的智能框架，称为OlaGPT。OlaGPT仔细研究了认知架构框架，并提出模拟人类认知的某些方面。该框架涉及近似不同认知模块，包括注意力、记忆、推理、学习以及相应的调度和决策机制。受人类主动学习机制启发，它提出了一个学习单元来记录先前的错误和专家意见，并动态地参考它们以增强解决类似问题的能力。该论文还概述了人类问题解决的常见有效推理框架，并相应设计了思维链（COT）模板。还提出了一个全面的决策机制，以最大化模型准确性。OlaGPT的有效性已在多个推理数据集上进行了严格评估，实验结果显示OlaGPT超越了最先进的基准，展示了其卓越性能。我们的OlaGPT实现可在GitHub上获得：https://github.com/oladata-team/OlaGPT。

English

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.

OlaGPT：赋予LLM人类般的问题解决能力

OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities

摘要

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