Title: Efficient Self-Review Framework for Enhancing Instruction Following Capability of LLM
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Instruction following, self-correction

Abstract: Large language models (LLMs) often struggle with faithfully following complex instructions, particularly when adherence requires satisfying multiple structural and content-level constraints. While recent work has explored iterative revision or external evaluation to address these limitations, such approaches either incur high computational costs or degrade generation quality through excessive revisions. We introduce Re5, a self-evaluation and revision framework that enables LLMs to improve instruction adherence in a scalable and quality-preserving manner. Re5 decomposes instructions into task and constraint components, performs structure-aware evaluations to prevent error accumulation, and applies selective, constraint-specific revisions. Crucially, this process yields refined outputs that can be fed back into alignment tuning, establishing a data-centric loop for continual improvement. Experiments show that Re5 achieves instruction-following performance comparable to models trained on data from closed-source generators such as GPT-4o-mini, while requiring significantly less data and maintaining high response quality (64.24% win rate over unrevised responses). These results highlight Re5 as a step toward self-improving LLMs, demonstrating how fine-grained self-evaluation can serve as an effective signal for alignment without heavy reliance on external supervision.