Towards Robust Calibration Dataset for Domain-General Mixture of Experts Pruning

arXiv:2610.15578 · cs.CL · Submitted 2026-10-11 · Read on arXiv

cs.CL

Submitted: 2026-10-11

Updated: 2026-10-11

Terminology

• Calibration — 1× in this paper · explained in 7 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes

Sources

• Task-Specific Expert Pruning for Sparse Mixture-of-Experts
• On the Representation Collapse of Sparse Mixture of Experts
• Training Verifiers to Solve Math Word Problems
• DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
• Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
• The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
• Measuring Massive Multitask Language Understanding
• Measuring Mathematical Problem Solving With the MATH Dataset
• Large Language Model Pruning
• Beware of Calibration Data for Pruning Large Language Models
• Mixtral of Experts
• SlimMoE: Structured Compression of Large MoE Models via Expert Slimming and Distillation
• gpt-oss-120b & gpt-oss-20b Model Card
• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
• MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router
• MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation

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