发表机构
North Carolina State University; Amazon Web Services（北卡罗来纳州立大学; 亚马逊网络服务）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对自然语言到逻辑公式翻译准确率提升难题，提出分层一致性蒸馏方法，经实验在Pass@K与等价逻辑相似度指标上获显著稳定提升，推进了逻辑翻译技术发展。
AI 中文摘要
神经符号推理通过结合大语言模型（LLMs）与符号求解器，在解决复杂推理任务中已展现出良好的应用前景。尽管该方法颇具潜力，但仍存在一个核心挑战：提升自然语言到逻辑公式的翻译准确率。当前方法主要依赖提示工程，难以在不同领域和输入格式间扩展。受其他模型适配与对齐应用中微调成功案例的启发，我们提出一种基于微调的分层一致性蒸馏方法：（1）使用前沿大语言模型为每个输入生成K个逻辑翻译结果，并按语义等价性对其聚类；（2）基于熵水平，分别采用多数投票（低熵）、LLM作为评判者（中熵）或统一/弃权（不执行）（高熵）处理；（3）利用选定的伪标签微调一个较小模型。实验结果显示，该方法在Pass@K指标及我们提出的等价逻辑相似度指标上均实现显著且稳定的提升，证明了通过一致性蒸馏推进逻辑翻译的潜力。
英文摘要
Neurosymbolic reasoning has shown promising success in addressing complex reasoning tasks by combining large language models (LLMs) and symbolic solvers. While this approach shows promise, a fundamental challenge remains: improving the accuracy of translations from natural language to logical formulas. Current methods predominantly rely on prompt engineering, which is difficult to scale across different domains and input formats. Drawing inspiration from the success of fine-tuning in other model adaptation and alignment applications, we propose a fine-tuning-based Stratified Consistency Distillation approach: (1) We generate K logical translations per input using a frontier LLM and cluster them by semantic equivalence (2) Based on the entropy level, we apply majority voting (low entropy), LLM-as-a-Judge (medium entropy), or unification/abstention (high entropy), and (3) fine-tune a smaller model using the selected pseudo-labels. Our experiments show significant and consistent improvements in both Pass@K and our novel Equivalent Logical Similarity metrics, demonstrating the potential of advancing logical translation through consistency distillation.
