195 citations · 458 across the 33 of their papers we have counts for
5 papers · 1 filter
Think Before You Prune: Self-Reflective Structured Pruning for Reasoning Language Models
Ziyan Wang, Enmao Diao, Qi Le +5
Reasoning LLMs (RLMs) such as OpenAI o1, DeepSeek-R1, and Qwen3 deliver strong multi-step reasoning through chain-of-thought generation, but their large model sizes and lengthy dec…
Toward Unifying Group Fairness Evaluation from a Sparsity Perspective
Zhecheng Sheng, Jiawei Zhang, Enmao Diao
Ensuring algorithmic fairness remains a significant challenge in machine learning, particularly as models are increasingly applied across diverse domains. While numerous fairness c…
From Local to Global: Revisiting Structured Pruning Paradigms for Large Language Models
Ziyan Wang, Enmao Diao, Qi Le +6
Structured pruning is a practical approach to deploying large language models (LLMs) efficiently, as it yields compact, hardware-friendly architectures. However, the dominant local…
OBCache: Optimal Brain KV Cache Pruning for Efficient Long-Context LLM Inference
Yuzhe Gu, Xiyu Liang, Jiaojiao Zhao +1
Large language models (LLMs) with extended context windows enable powerful applications but impose significant memory overhead, as caching all key-value (KV) states scales linearly…
Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing
Qi Le, Enmao Diao, Ziyan Wang +4
We introduce Probe Pruning (PP), a novel framework for online, dynamic, structured pruning of Large Language Models (LLMs) applied in a batch-wise manner. PP leverages the insight…