IF-RewardBench uses preference graphs for listwise evaluation of judge models on instruction-following, exposing deficiencies in current judges and achieving stronger correlation with downstream task performance than existing benchmarks.
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IF-CRITIC is a fine-grained LLM critic using checklist generation and constraint-level preference optimization that outperforms strong baselines like o4-mini in instruction-following evaluation while enabling lower-cost model optimization.
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IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation
IF-RewardBench uses preference graphs for listwise evaluation of judge models on instruction-following, exposing deficiencies in current judges and achieving stronger correlation with downstream task performance than existing benchmarks.
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IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation
IF-CRITIC is a fine-grained LLM critic using checklist generation and constraint-level preference optimization that outperforms strong baselines like o4-mini in instruction-following evaluation while enabling lower-cost model optimization.