arXiv AI
Coding LLMs Can Be Harnessed to Learn Interpretable Models from Data
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arXiv AI
Jul 7
RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Models
arXiv:2510. 19698v3 Announce Type: replace Abstract: Large Language Models (LLMs) can propose rules in natural language, sidestepping the need for a predefined predicate space in traditional rule learning.
arXiv Machine Learning
Oct 8
From Prompts to Trees: Effective LLM-Guided Tree Generation for Few-Shot Tabular Classification
The paper introduces a three‑stage framework that uses Large Language Models (LLMs) to generate rules and assemble them into decision trees for few‑shot tabular classification. By distilling LLM knowledge into interpretable trees, the method avoids the high inference costs and limited interpretability of direct LLM application while outperforming traditional decision trees in low‑data settings. Experiments on real‑world datasets show superior accuracy and interpretability with lower prompting overhead compared to existing baselines.
Hugging Face Trending Papers
Oct 7
From Prompts to Trees: Effective LLM-Guided Tree Generation for Few-Shot Tabular Classification
While Large Language Models (LLMs) possess rich world knowledge and impressive generalization capabilities, their direct application to tabular data classification is hindered by high inference costs...
arXiv AI
Jun 24
Rule2Text: A Framework for Generating and Evaluating Natural Language Explanations of Knowledge Graph Rules
arXiv:2508. 10971v2 Announce Type: replace-cross Abstract: Knowledge graphs (KGs) can be enhanced through rule mining; however, the resulting logical rules are often difficult for humans to interpret due to their inherent complexity and the idiosyncratic labeling conventions of individual KGs.
arXiv AI
Jun 3
Distilling Answer-Set Programming Rules from LLMs for Neurosymbolic Visual Question Answering
arXiv:2606. 03269v1 Announce Type: new Abstract: Visual Question Answering (VQA) is the task of answering questions about images, requiring the integration of multimodal input and reasoning.
arXiv Computation and Language
Aug 25
Beyond Factual Knowledge: Benchmarking and Learning Step-Level Procedural Rule Reasoning in Large Language Models
arXiv:2608.22753v1 Announce Type: new Abstract: Large language models (LLMs) excel at text understanding and generation, yet still struggle to reliably understand and apply externally provided proced...