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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.11336 |
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| _version_ | 1866916652672614400 |
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| author | Diallo, Aissatou Bikakis, Antonis Dickens, Luke Hunter, Anthony Miller, Rob |
| author_facet | Diallo, Aissatou Bikakis, Antonis Dickens, Luke Hunter, Anthony Miller, Rob |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11336 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rule-Guided Feedback: Enhancing Reasoning by Enforcing Rule Adherence in Large Language Models Diallo, Aissatou Bikakis, Antonis Dickens, Luke Hunter, Anthony Miller, Rob Computation and Language 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. |
| title | Rule-Guided Feedback: Enhancing Reasoning by Enforcing Rule Adherence in Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.11336 |