Current evaluation methods for large language models are coarse-grained and decoupled from generation, producing generic explanations that fail to provide actionable feedback for model improvement. We propose CriticGen, a fine-grained, generation-aware evaluation framework that turns evaluation into actionable control for answer improvement. CriticGen first generates sample-specific evaluation dimensions and scoring criteria, then uses the induced rubric to jointly produce a score, a reason, an executable refinement suggestion, and a refined answer. Experiments show that fine-grained evaluation should be both instance-specific and actionable. CriticGen improves human-rated rubric relevance/coverage from 3.33/4.03 to 3.97/4.24. On the score-consensus-filtered RefineData test set, the best CriticGen variant reaches 0.9556/0.9560 Pearson/Spearman agreement with the reference scores and improves reason/suggestion F1 to 0.7554/0.7900. Independent validation provides additional support: on 180 human-scored instances, CriticGen-Qwen2.5-7B obtains higher observed score correlations than GPT-5, with 0.8868/0.8969 Pearson/Spearman versus 0.8542/0.8615. Crucially, its feedback translates into substantial answer improvement, raising accuracy from 45.36% to 91.26% on 5,000 examples with reliable gold answers, with 47.30% of answers corrected from Wrong→Right and only 1.40% degraded from Right→Wrong.

With the development of medical digitization, the extraction and structuring of Electronic Medical Records (EMRs) have become challenging but fundamental tasks. How to accurately and automatically extract structured information from medical dialogues is especially difficult because the information needs to be inferred from complex interactions between the doctor and the patient. To this end, in this paper, we propose a speaker-aware co-attention framework for medical dialogue information extraction. To better utilize the pre-trained language representation model to perceive the semantics of the utterance and the candidate item, we develop a speaker-aware dialogue encoder with multi-task learning, which considers the speaker’s identity into account. To deal with complex interactions between different utterances and the correlations between utterances and candidate items, we propose a co-attention fusion network to aggregate the utterance information. We evaluate our framework on the public medical dialogue extraction datasets to demonstrate the superiority of our method, which can outperform the state-of-the-art methods by a large margin. Codes will be publicly available upon acceptance.