3 papers
cs.CL2025
KVReviver: Reversible KV Cache Compression with Sketch-Based Token Reconstruction
Aomufei Yuan, Zhiming Wang, Ruijie Miao +8
As the context length of current large language models (LLMs) rapidly increases, the memory demand for the Key-Value (KV) cache is becoming a bottleneck for LLM deployment and batc…
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
Inverse IFEval: Can LLMs Unlearn Stubborn Training Conventions to Follow Real Instructions?
Qinyan Zhang, Xinping Lei, Ruijie Miao +18
Large Language Models (LLMs) achieve strong performance on diverse tasks but often exhibit cognitive inertia, struggling to follow instructions that conflict with the standardized…