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

2025-08-29

AI Summary

Large language models (LLMs) often produce reasoning outputs compromised by conceptual flaws—such as missing premises or logical discontinuities—yet existing mitigation strategies rely heavily on manually crafted demonstrations or rigid chain-of-thought templates. Method: We propose GIER, a demonstration-free, template-agnostic framework that models reasoning gaps via natural-language quality criteria (e.g., “lack of premise support”, “conclusion-reasoning misalignment”) and enables LLMs to autonomously detect flaws, generate critical feedback, and iteratively refine outputs. Its core components include a self-reflective prompting mechanism, multi-round iterative revision, and cross-model transferable semantic gap modeling. Contribution/Results: Evaluated across three high-reasoning tasks and four mainstream LLMs, GIER significantly improves reasoning grounding, logical consistency, and alignment fidelity—without sacrificing task accuracy.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Improving LLM outputs through self-reflection and revision
• Addressing reasoning gaps using natural language descriptions
• Enhancing rationale quality without degrading task accuracy

Innovation

• Self-reflection and revision framework
• Iterative critique and refinement process
• Natural language gap descriptions