弱监督下自动发现数学推理中间符号指令，提升神经符号系统泛化能力
Advanced Weakly-Supervised Formula Exploration for Neuro-Symbolic Mathematical Reasoning
- 基于问题输入和最终输出进行弱监督，自动探索中间符号指令
- 在Mathematics数据集上显著提升复杂数学推理任务的准确率
- 适用于缺乏标注中间步骤的复杂逻辑推理场景，适合算法研究者
近年来，神经符号方法已成为增强人工智能系统进行抽象、逻辑与定量推理的重要手段，具备更高的精度与可控性。现有方法通过机器学习模型显式或隐式预测中间标签以提供符号指令，但这些标签并非所有任务都可获取，且大语言模型（LLMs）也未必能稳定生成有效符号指令。已有研究尝试让系统自主发现最优符号指令，但在庞大搜索空间或高难度推理任务中表现受限。为此，本文提出一种先进的弱监督公式探索方法，利用问题输入与最终输出对中间标签进行弱监督引导。在Mathematics数据集上的实验表明，该方法在多个维度上均展现出显著有效性。
原文摘要 · Abstract (English)
In recent years, neuro-symbolic methods have become a popular and powerful approach that augments artificial intelligence systems with the capability to perform abstract, logical, and quantitative deductions with enhanced precision and controllability. Recent studies successfully performed symbolic reasoning by leveraging various machine learning models to explicitly or implicitly predict intermediate labels that provide symbolic instructions. However, these intermediate labels are not always prepared for every task as a part of training data, and pre-trained models, represented by Large Language Models (LLMs), also do not consistently generate valid symbolic instructions with their intrinsic knowledge. On the other hand, existing work developed alternative learning techniques that allow the learning system to autonomously uncover optimal symbolic instructions. Nevertheless, their performance also exhibits limitations when faced with relatively huge search spaces or more challenging reasoning problems. In view of this, in this work, we put forward an advanced practice for neuro-symbolic reasoning systems to explore the intermediate labels with weak supervision from problem inputs and final outputs. Our experiments on the Mathematics dataset illustrated the effectiveness of our proposals from multiple aspects.
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