标题： 神经符号人工智能方法在可解释逻辑推理中的比较研究 显示英文标题

标题： A Comparative Study of Neurosymbolic AI Approaches to Interpretable Logical Reasoning

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，仍然是大型语言模型（LLMs）的挑战。当前的LLMs无法进行确定性推理，并且不可解释。因此，神经符号人工智能最近引起了广泛关注，它试图将逻辑融入神经网络中。我们首先确定了两种改进逻辑推理的神经符号方法：（i）集成方法，其中符号推理包含在神经网络中；（ii）混合方法，其中与神经网络分开的符号求解器执行符号推理。这两种方法都包含在特定领域逻辑推理基准测试中表现良好的AI系统。然而，它们在领域无关基准测试上的表现研究不足。据我们所知，还没有对对比方法进行比较，以回答以下问题：哪种方法在开发通用逻辑推理方面更有前景？为了分析它们的潜力，介绍了以下最佳领域的无关模型：逻辑神经网络（LNN），它使用集成方法；以及LLM-符号求解器（LLM-SS），它使用混合方法。使用这两种模型作为每种方法的案例研究和代表，我们的分析表明，混合方法在开发通用逻辑推理方面更有前景，因为（i）它的推理链更具可解释性，（ii）它保留了现有LLMs的功能和优势。为了支持未来使用混合方法的工作，我们提出了一种基于LLM-SS的可推广框架，该框架设计为模块化、模型无关、领域无关，并且需要很少甚至不需要人工输入。 显示英文摘要

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.