arXiv：cs.LG（机器学习，全量分类）· Samuele Bortolotti, Weixin Chen, Han Zhao, Andrea Passerini, Stefano Teso, Antonio Vergari·· 14 小时前AI 评分43
用 LLM 自动形式化神经符号预测器的约束
Auto-Formalizing Neuro-Symbolic Predictors
AI 导读
研究团队提出 auto-nesy-bench 基准，用于评估 LLM 将文本知识自动形式化为符号约束的效果及其对神经符号（NeSy）预测器下游准确率的影响。跨多个领域的评测显示，LLM 生成的公式常与人类专家提供的约束相似，且在语法有效时能带来高质量的下游预测。代码与基准已公开。
正文
Abstract:Neuro-Symbolic (NeSy) predictors incorporate prior knowledge into the prediction process of neural networks, ensuring that outputs satisfy specified constraints, making them particularly suitable for high-stakes applications where compliance with domain knowledge is essential. A key bottleneck in this paradigm is the acquisition of symbolic constraints: encoding domain knowledge into logical formulas remains a manual and expert-intensive process. In this work, we investigate the extent to which auto-formalization via LLMs can systematically translate textual knowledge into symbolic knowledge that can be plugged into NeSy predictors. To this end, we introduce auto-nesy-bench, a new benchmark for evaluating constraint formalization and its impact on downstream accuracy of NeSy predictors. Through an extensive evaluation across several domains, we find that LLMs can formalize constraints to a meaningful extent, generating formulas that are often similar to those provided by human experts. Moreover, when the generated formulas are syntactically valid, they can lead to high-quality downstream predictions. The code and benchmark are available at this https URL.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.01519 [cs.LG] |
| (or arXiv:2610.01519v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.01519 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Samuele Bortolotti [view email]
[v1]
Thu, 1 Oct 2026 11:53:06 UTC (586 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org