GIER: Gap-Driven Self-Refinement for Large Language Models

• URL: http://arxiv.org/abs/2509.00325v1
• Date: Sat, 30 Aug 2025 02:54:08 GMT
• Title: GIER: Gap-Driven Self-Refinement for Large Language Models
• Authors: Rinku Dewri,
• Abstract summary: GIER (Gap-driven Iterative Enhancement of Responses) is a framework for improving large language model (LLM) outputs through self-reflection and revision.<n>GIER improves rationale quality, grounding, and reasoning alignment without degrading task accuracy.<n>Our analysis demonstrates that models can not only interpret abstract conceptual gaps but also translate them into concrete reasoning improvements.
• Score: 0.8460698440162889
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce GIER (Gap-driven Iterative Enhancement of Responses), a general framework for improving large language model (LLM) outputs through self-reflection and revision based on conceptual quality criteria. Unlike prompting strategies that rely on demonstrations, examples, or chain-of-thought templates, GIER utilizes natural language descriptions of reasoning gaps, and prompts a model to iteratively critique and refine its own outputs to better satisfy these criteria. Across three reasoning-intensive tasks (SciFact, PrivacyQA, and e-SNLI) and four LLMs (GPT-4.1, GPT-4o Mini, Gemini 1.5 Pro, and Llama 3.3 70B), GIER improves rationale quality, grounding, and reasoning alignment without degrading task accuracy. Our analysis demonstrates that models can not only interpret abstract conceptual gaps but also translate them into concrete reasoning improvements.

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