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.
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.
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: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: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: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...
arXiv:2601. 22324v3 Announce Type: replace Abstract: Modern clinical practice relies on evidence-based guidelines implemented as compact scoring systems composed of a small number of interpretable decision rules.
Despite the rapid progress of LLM-based agents for planning, code generation, and debugging, their practical value for tabular machine learning remains underexplored. In this paper, we investigate a c...
arXiv:2401.09918v2 Announce Type: replace Abstract: Rule set learning has recently been frequently revisited because of its interpretability. Existing methods have several shortcomings though. First,...
arXiv:2605. 24417v2 Announce Type: replace Abstract: Supervised classification on tabular data remains a central machine learning task, but its dependence on large labeled datasets limits its applicability in data-scarce settings.
The paper investigates whether large language models (LLMs) truly grasp logical structure or merely rely on surface semantics. By evaluating models on logically equivalent inputs, the authors find that many LLMs fail to produce consistent predictions and struggle with unseen logical forms. They introduce LoGIST, a framework that maps inputs to decision diagrams and uses these logical embeddings to guide LLM reasoning, thereby reducing inconsistency and improving generalization.
arXiv:2609.16309v1 Announce Type: new Abstract: Despite the rapid progress of LLM-based agents for planning, code generation, and debugging, their practical value for tabular machine learning remains...