Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2503.11336 v1 pith:SIDDBSMS submitted 2025-03-14 cs.CL

Aissatou Diallo , Antonis Bikakis , Luke Dickens , Anthony Hunter , Rob Miller This is my paper · ORCID
classification cs.CL
keywords feedbackadherenceenhanceframeworkinformationlanguagelargemodel
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Meflex: A Multi-agent Scaffolding System for Entrepreneurial Ideation Iteration via Nonlinear Business Plan Writing

    cs.HC 2026-02 conditional novelty 5.0 of 10

    A nonlinear, LLM-scaffolded business-plan writing tool with reflection and meta-reflection improves perceived usability and helps students iterate ideas in a 30-participant study.

Pith tools