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. Yet many LLM-based approaches ignore interactions among rules, and the opportunity to couple LLMs with probabilistic rule learning for robust inference remains underexplored. We present RLIE, a unified framework that integrates LLMs with probabilistic modeling to learn a set of weighted rules. RLIE has four stages: (1) Rule generation, where an LLM proposes and filters candidates; (2) Logisti
The continuous advancements in Large Language Models necessitate increasingly sophisticated methods for rule generation and integration to enhance their decision-making capabilities and robustness.
This development allows LLMs to generate and refine rules more effectively, moving beyond natural language suggestions to probabilistic, weighted rule sets that can dramatically improve AI system performance and reliability.
LLMs can now generate and integrate rules with probabilistic modeling, leading to more robust inference and a departure from approaches that ignore interactions among rules.
- · AI developers
- · Enterprises deploying LLMs
- · Probabilistic AI research
- · Traditional rule-based AI systems
- · LLM applications relying solely on natural language rule suggestions
Improved performance and reliability of AI systems utilizing LLMs for complex decision-making.
Expansion of LLM applications into domains requiring high-trust and robust inferential capabilities.
Accelerated development of autonomous AI agents capable of self-improving their rule sets and operational logic.
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Read at arXiv cs.AI