arXiv cs.AI· Aditya Kumar, Cassandra A. Cohen, William W. Cohen·· 3 天前AI 评分34
编码 LLM 可被用于从数据中学习可解释模型
Coding LLMs Can Be Harnessed to Learn Interpretable Models from Data
AI 导读
研究者提出一种简单 harness，反复提示编码 LLM 根据标注样本生成规则集中的下一条规则，从而从数据中学习可解释模型。该方法在表格数据上与 SOTA 规则学习方法相当甚至更优，在小训练集上优势明显，在文本任务上大幅领先，包括击败另一个基于 LLM 的规则学习器。由于编码智能体不受高度受限假设空间约束，该思路还直接催生了新型可解释模型，并引入“构建可解释分类器”这一新编码基准类别。
正文 · 原文
Abstract:Since compact, interpretable classifiers correspond to simple programs, it is natural to ask if LLM-based coding agents can be used to learn interpretable models from data. In this paper we present a simple harness that repeatedly prompts a coding LLM to produce the next rule in a rule set, given a sample of labeled examples. We show that this approach is competitive with, and often superior to, state-of-the-art rule-learning methods on tabular data, especially with small training sets, and wins by large margins on text, including against another LLM-based rule learner; that this approach outperforms standard harnesses, and that the approach immediately leads to new kinds of interpretable models, since coding agents are not restricted to search a highly restricted hypothesis space. In addition to the advances made in learning interpretable models, these results are of interest because they introduce a new class of coding benchmarks - construction of interpretable classifiers - which includes quantitative measurements of output quality (i.e., test-set error) and existing mature ``specialist coding algorithms'' (i.e., existing learning methods).
| Comments: | Main Text: 10 pages, 4 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.14006 [cs.LG] |
| (or arXiv:2610.14006v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14006 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Aditya Kumar [view email]
[v1]
Mon, 28 Sep 2026 17:05:42 UTC (493 KB)
来源：arXiv cs.AI · arxiv.org