Rubric-as-Experts: Case-Specific MQM Rubrics for Translation Error Span Detection

arXiv:2606.21559 · cs.CL · Submitted 2026-06-19 · Read on arXiv

cs.CL

Submitted: 2026-06-19

Updated: 2026-09-26

Terminology

• Ablation — 11× in this paper · explained in 1 episode
• Accuracy — 9× in this paper · explained in 1 episode
• Benchmark — 9× in this paper · explained in 2 episodes
• Error Localization — 6× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Core — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Multidimensional Quality Metrics — 2× in this paper
• Test-time Scaling — 2× in this paper · explained in 2 episodes
• Thinking mode — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sample Complexity — 1× in this paper · explained in 15 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• GPT-4 Technical Report
• Deepchecks: Evaluating Retrieval-Augmented Generation (RAG)
• Adaptive Computation Time for Recurrent Neural Networks
• Test-Time Learning for Large Language Models
• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
• Qwen3 Technical Report
• A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?

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