2 papers
cs.LG2025
MergeMoE: Efficient Compression of MoE Models via Expert Output Merging
Ruijie Miao, Yilun Yao, Zihan Wang +5
The Mixture-of-Experts (MoE) technique has proven to be a promising solution to efficiently scale the model size, which has been widely applied in recent LLM advancements. However,…
cs.CL2025
Proof2Hybrid: Automatic Mathematical Benchmark Synthesis for Proof-Centric Problems
Yebo Peng, Zixiang Liu, Yaoming Li +6
Evaluating the mathematical capability of Large Language Models (LLMs) is a critical yet challenging frontier. Existing benchmarks fall short, particularly for proof-centric proble…