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arXiv · 2503.11336

Rule-Guided Feedback: Enhancing Reasoning by Enforcing Rule Adherence in Large Language Models

Aissatou Diallo·Antonis Bikakis·Luke Dickens·Anthony Hunter·Rob Miller
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Abstract

In this paper, we introduce Rule-Guided Feedback (RGF), a framework designed to enhance Large Language Model (LLM) performance through structured rule adherence and strategic information seeking. RGF implements a teacher-student paradigm where rule-following is forced through established guidelines. Our framework employs a Teacher model that rigorously evaluates each student output against task-specific rules, providing constructive guidance rather than direct answers when detecting deviations. This iterative feedback loop serves two crucial purposes: maintaining solutions within defined constraints and encouraging proactive information seeking to resolve uncertainties. We evaluate RGF on diverse tasks including Checkmate-in-One puzzles, Sonnet Writing, Penguins-In-a-Table classification, GSM8k, and StrategyQA. Our findings suggest that structured feedback mechanisms can significantly enhance LLMs' performance across various domains.

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Aissatou Diallo, Antonis Bikakis, Luke Dickens, Anthony Hunter, Rob Miller. 2025-03-14. Rule-Guided Feedback: Enhancing Reasoning by Enforcing Rule Adherence in Large Language Models. https://arxiv.org/abs/2503.11336

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