LLMs for Relational Reasoning: How Far are We?
作者: Zhiming Li, Yushi Cao, Xiufeng Xu, Junzhe Jiang, Xu Liu, Yon Shin Teo, Shang-wei Lin, Yang Liu
分类: cs.AI, cs.CL
发布日期: 2024-01-17
备注: Accepted by The First International Workshop on Large Language Models for Code (ICSE 2024)
💡 一句话要点
评估大型语言模型的推理能力，揭示其局限性
🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture) 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 大型语言模型 推理能力 归纳逻辑编程 神经程序归纳 因果推理 性能评估 复杂推理任务
📋 核心要点
- 现有的文本和数字推理基准过于简单，无法全面评估LLMs的推理能力。
- 本文通过归纳逻辑编程基准深入评估LLMs的推理能力，强调其在复杂推理任务中的不足。
- 实验结果显示，LLMs在推理能力上显著低于小型神经程序归纳系统，表现出较低的性能和泛化能力。
📝 摘要（中文）
大型语言模型（LLMs）在自然语言处理等多个领域取得了突破性进展，但其推理能力仍然存在不足。本文通过基于归纳逻辑编程（ILP）基准的深入评估，揭示了当前LLMs在推理任务中的表现远不及小型神经程序归纳系统。研究表明，LLMs在处理需要严格因果逻辑的任务时，表现出较低的性能和泛化能力。
🔬 方法详解
问题定义：本文旨在评估大型语言模型在推理任务中的能力，现有方法在推理基准上表现不佳，无法充分反映其推理能力的真实水平。
核心思路：通过采用归纳逻辑编程（ILP）基准，本文提供了一种更具挑战性的评估方式，旨在揭示LLMs在复杂推理任务中的局限性。
技术框架：研究首先定义了推理任务的标准，然后对多种先进的LLMs进行评估，比较其在ILP基准上的表现，最后与小型神经程序归纳系统进行对比。
关键创新：本文的创新在于使用ILP基准作为评估工具，强调了LLMs在因果推理方面的不足，与传统的文本和数字推理基准相比，提供了更深入的洞察。
关键设计：在实验中，采用自然语言提示和真值矩阵提示两种方式进行评估，关注模型在独立同分布（IID）和分布外（OOD）样本上的表现，确保评估的全面性和准确性。
🖼️ 关键图片
📊 实验亮点
实验结果显示，当前的LLMs在ILP基准上的表现显著低于小型神经程序归纳系统，具体而言，LLMs的推理能力在自然语言提示和真值矩阵提示下均表现出较低的性能和泛化能力，强调了其在复杂推理任务中的局限性。
🎯 应用场景
该研究的结果对人工智能领域的推理系统设计具有重要意义，尤其是在需要复杂推理和决策的应用场景中，如自动驾驶、智能助手和复杂系统模拟等。未来，改进LLMs的推理能力将推动更广泛的智能应用。
📄 摘要（原文）
Large language models (LLMs) have revolutionized many areas (e.g. natural language processing, software engineering, etc.) by achieving state-of-the-art performance on extensive downstream tasks. Aiming to achieve robust and general artificial intelligence, there has been a surge of interest in investigating the reasoning ability of the LLMs. Whereas the textual and numerical reasoning benchmarks adopted by previous works are rather shallow and simple, it is hard to conclude that the LLMs possess strong reasoning ability by merely achieving positive results on these benchmarks. Recent efforts have demonstrated that the LLMs are poor at solving sequential decision-making problems that require common-sense planning by evaluating their performance on the reinforcement learning benchmarks. In this work, we conduct an in-depth assessment of several state-of-the-art LLMs' reasoning ability based on the inductive logic programming (ILP) benchmark, which is broadly recognized as a representative and challenging measurement for evaluating logic program induction/synthesis systems as it requires inducing strict cause-effect logic to achieve robust deduction on independent and identically distributed (IID) and out-of-distribution (OOD) test samples. Our evaluations illustrate that compared with the neural program induction systems which are much smaller in model size, the state-of-the-art LLMs are much poorer in terms of reasoning ability by achieving much lower performance and generalization using either natural language prompting or truth-value matrix prompting.