6 papers
Adaptive Context Parallelism for Production LLM Serving
Jiarui Guo, Rongle Wang, Peijun Huang +7
As LLM context windows expand and input sequences grow longer, serving systems face increasing computational and memory demands. Context parallelism (CP), which partitions the inpu…
Beyond Token Eviction: Mixed-Dimension Budget Allocation for Efficient KV Cache Compression
Ruijie Miao, Zhiming Wang, Wang Li +4
Key-value (KV) caching is widely used to accelerate transformer inference, but its memory cost grows linearly with input length, limiting long-context deployment. Existing token ev…
Rethinking Retrieval-Augmentation as Synthesis: A Query-Aware Context Merging Approach
Jiarui Guo, Yuemeng Xu, Zongwei Lv +6
Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to extend their existing knowledge by dynamically incorporating external information. However, practical d…
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,…
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…
An Efficient Inference Framework for Early-exit Large Language Models
Ruijie Miao, Yihan Yan, Xinshuo Yao +1
Building efficient inference framework has gained increasing interests for research community. Early-exit models, a variant of LLMs, improves the inference efficiency of LLMs by sk…