IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation

• URL: http://arxiv.org/abs/2511.01014v1
• Date: Sun, 02 Nov 2025 17:06:49 GMT
• Title: IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation
• Authors: Bosi Wen, Yilin Niu, Cunxiang Wang, Pei Ke, Xiaoying Ling, Ying Zhang, Aohan Zeng, Hongning Wang, Minlie Huang,
• Abstract summary: We propose IF-CRITIC, an evaluation model for instruction following in Large Language Models.<n>With the scalable reward signals provided by IF-CRITIC, LLMs can achieve substantial performance gains in instruction-following optimization.
• Score: 87.38454788767545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Instruction following is a fundamental ability of Large Language Models (LLMs), requiring their generated outputs to follow multiple constraints imposed in input instructions. Numerous studies have attempted to enhance this ability through preference optimization or reinforcement learning based on reward signals from LLM-as-a-Judge. However, existing evaluation models for instruction following still possess many deficiencies, such as substantial costs and unreliable assessments. To this end, we propose IF-CRITIC, an LLM critic that can provide efficient and reliable assessments of constraint following in the instructions. We first develop a checklist generator to decompose instructions and generate constraint checklists. With the assistance of the checklists, we collect high-quality critique training data through a multi-stage critique filtering mechanism and employ a constraint-level preference optimization method to train IF-CRITIC. Extensive experiments demonstrate that the evaluation performance of IF-CRITIC can beat strong LLM-as-a-Judge baselines, including Deepseek-R1 and o4-mini. With the scalable reward signals provided by IF-CRITIC, LLMs can achieve substantial performance gains in instruction-following optimization under lower computational overhead compared to strong LLM critic baselines.

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