45 citations · 53 across the 23 of their papers we have counts for
5 papers · 1 filter
AnTKV: Anchor Token-Aware Sub-Bit Vector Quantization for KV Cache in Large Language Models
Zeyu Li, Chuanfu Xiao, Yang Wang +6
Quantization has emerged as an effective and lightweight solution to reduce the memory footprint of the KV cache in Large Language Models. Nevertheless, minimizing the accuracy deg…
Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research
Xiang Liu, Penglei Sun, Shuyan Chen +7
The rapid advancement of perovskite solar cells (PSCs) has led to an exponential growth in research publications, creating an urgent need for efficient knowledge management and rea…
Mediator: Memory-efficient LLM Merging with Less Parameter Conflicts and Uncertainty Based Routing
Kunfeng Lai, Zhenheng Tang, Xinglin Pan +7
Model merging aggregates Large Language Models (LLMs) finetuned on different tasks into a stronger one. However, parameter conflicts between models leads to performance degradation…
Semantic Integrity Matters: Benchmarking and Preserving High-Density Reasoning in KV Cache Compression
Xiang Liu, Zhenheng Tang, Hong Chen +6
While Key-Value (KV) cache compression is essential for efficient LLM inference, current evaluations disproportionately focus on sparse retrieval tasks, potentially masking the deg…
ChunkKV: Semantic-Preserving KV Cache Compression for Efficient Long-Context LLM Inference
Xiang Liu, Zhenheng Tang, Peijie Dong +5
Large Language Models (LLMs) require significant GPU memory when processing long texts, with the key value (KV) cache consuming up to 70\% of total memory during inference. Althoug…