神经符号AI方法在可解释逻辑推理中的比较研究
A Comparative Study of Neurosymbolic AI Approaches to Interpretable Logical Reasoning
AI总结：
本研究比较了神经符号AI中整合与混合两种方法，发现混合方法（如LLM-SS）在通用逻辑推理上更具可解释性和优势，并提出了一个模块化、模型无关的通用框架。
摘要：
通用逻辑推理，定义为在领域无关任务上进行演绎推理的能力，对大型语言模型（LLMs）而言仍然是一个挑战。当前的LLMs无法进行确定性推理，且不具备可解释性。因此，近年来人们对神经符号AI的兴趣激增，该领域试图将逻辑融入神经网络。我们首先识别出两种主要的改进逻辑推理的神经符号方法：（i）整合方法，包含符号推理内嵌于神经网络中的模型；（ii）混合方法，包含由独立于神经网络的符号求解器执行符号推理的模型。两者都包含在特定领域逻辑推理基准上取得有前景结果的AI系统。然而，它们在领域无关基准上的表现研究不足。据我们所知，尚未有对比这两种方法并回答以下问题的研究：哪种方法更有前景用于发展通用逻辑推理？为了分析它们的潜力，引入了以下同类最佳的领域无关模型：逻辑神经网络（LNN），采用整合方法；以及LLM-符号求解器（LLM-SS），采用混合方法。以这两种模型作为案例研究和每种方法的代表，我们的分析表明，混合方法更有前景用于发展通用逻辑推理，因为（i）其推理链更具可解释性，且（ii）它保留了现有LLM的能力和优势。为了支持未来采用混合方法的工作，我们提出了一个基于LLM-SS的通用框架，该框架在设计上模块化、模型无关、领域无关，并且几乎不需要人工输入。
英文摘要：
General logical reasoning, defined as the ability to reason deductively on domain-agnostic tasks, continues to be a challenge for large language models (LLMs). Current LLMs fail to reason deterministically and are not interpretable. As such, there has been a recent surge in interest in neurosymbolic AI, which attempts to incorporate logic into neural networks. We first identify two main neurosymbolic approaches to improving logical reasoning: (i) the integrative approach comprising models where symbolic reasoning is contained within the neural network, and (ii) the hybrid approach comprising models where a symbolic solver, separate from the neural network, performs symbolic reasoning. Both contain AI systems with promising results on domain-specific logical reasoning benchmarks. However, their performance on domain-agnostic benchmarks is understudied. To the best of our knowledge, there has not been a comparison of the contrasting approaches that answers the following question: Which approach is more promising for developing general logical reasoning? To analyze their potential, the following best-in-class domain-agnostic models are introduced: Logic Neural Network (LNN), which uses the integrative approach, and LLM-Symbolic Solver (LLM-SS), which uses the hybrid approach. Using both models as case studies and representatives of each approach, our analysis demonstrates that the hybrid approach is more promising for developing general logical reasoning because (i) its reasoning chain is more interpretable, and (ii) it retains the capabilities and advantages of existing LLMs. To support future works using the hybrid approach, we propose a generalizable framework based on LLM-SS that is modular by design, model-agnostic, domain-agnostic, and requires little to no human input.
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