arXiv：cs.AI· Aditya Kumar, Cassandra A. Cohen, William W. Cohen·· 16 小时前
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框架，反复提示编码 LLM 在给定标注样本后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，在小训练集上尤其明显，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。作者称这引入了一类新的编码基准——构建可解释分类器。
正文
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