MERIT: Matching Expertise via Rubric-Informed Training for Reviewer Assignment

arXiv:2605.27865 · cs.CL · Submitted 2026-05-27 · Read on arXiv

cs.CL

Submitted: 2026-05-27

Updated: 2026-09-10

Comments: EMNLP 2026

Code: https://github.com/Luli3220/MERIT

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Core — 39× in this paper · explained in 2 episodes
• Accuracy — 16× in this paper · explained in 1 episode
• Consistency — 14× in this paper · explained in 3 episodes
• Reinforcement Learning — 11× in this paper · explained in 4 episodes
• Benchmark — 10× in this paper · explained in 2 episodes
• Fidelity — 9× in this paper · explained in 2 episodes
• LLM judge — 8× in this paper · explained in 2 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Diffusion Model — 7× in this paper · explained in 8 episodes
• Diffusion Models — 7× in this paper · explained in 6 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Two-Stage Framework — 6× in this paper · explained in 2 episodes
• Causal Inference — 5× in this paper · explained in 4 episodes
• GRPO — 5× in this paper · explained in 6 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Benchmarking — 3× in this paper · explained in 4 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Quantization — 3× in this paper · explained in 12 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Sparsity — 3× in this paper · explained in 4 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 3 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Pseudo-labels — 2× in this paper · explained in 2 episodes
• RAG — 2× in this paper · explained in 3 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Federated Learning — 1× in this paper · explained in 15 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Heterogeneous Graph — 1× in this paper · explained in 2 episodes
• Predictive Modeling — 1× in this paper · explained in 2 episodes
• Quantization-Aware Training — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Thinking mode — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• HealthBench: Evaluating Large Language Models Towards Improved Human Health
• AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following
• OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment
• RATE: Reviewer Profiling and Annotation-free Training for Expertise Ranking in Peer Review Systems
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• HybridFlow: A Flexible and Efficient RLHF Framework
• PLawBench: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice
• Qwen3 Technical Report
• Health-SCORE: Towards Scalable Rubrics for Improving Health-LLMs
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

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