51 citations · 242 across the 54 of their papers we have counts for
11 papers · 1 filter
Scaling LLM Speculative Decoding: Non-Autoregressive Forecasting in Large-Batch Scenarios
Luohe Shi, Zuchao Li, Lefei Zhang +3
Speculative decoding accelerates LLM inference by utilizing otherwise idle computational resources during memory-to-chip data transfer. Current speculative decoding methods typical…
XQuant: Achieving Ultra-Low Bit KV Cache Quantization with Cross-Layer Compression
Haoqi Yang, Yao Yao, Zuchao Li +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing tasks. However, their extensive memory requirements, particularly…
From Parameters to Performance: A Data-Driven Study on LLM Structure and Development
Suqing Wang, Zuchao Li, Luohe Shi +4
Large language models (LLMs) have achieved remarkable success across various domains, driving significant technological advancements and innovations. Despite the rapid growth in mo…
CoViPAL: Layer-wise Contextualized Visual Token Pruning for Large Vision-Language Models
Zicong Tang, Ziyang Ma, Suqing Wang +5
Large Vision-Language Models (LVLMs) process multimodal inputs consisting of text tokens and vision tokens extracted from images or videos. Due to the rich visual information, a si…
SmallKV: Small Model Assisted Compensation of KV Cache Compression for Efficient LLM Inference
Yi Zhao, Yajuan Peng, Cam-Tu Nguyen +4
KV cache eviction has emerged as an effective solution to alleviate resource constraints faced by LLMs in long-context scenarios. However, existing token-level eviction methods oft…
IAM: Efficient Inference through Attention Mapping between Different-scale LLMs
Yi Zhao, Zuchao Li, Hai Zhao
LLMs encounter significant challenges in resource consumption nowadays, especially with long contexts. Despite extensive efforts dedicate to enhancing inference efficiency, these m…
DAC: A Dynamic Attention-aware Approach for Task-Agnostic Prompt Compression
Yi Zhao, Zuchao Li, Hai Zhao +2
Task-agnostic prompt compression leverages the redundancy in natural language to reduce computational overhead and enhance information density within prompts, especially in long-co…
KV-Latent: Dimensional-level KV Cache Reduction with Frequency-aware Rotary Positional Embedding
Luohe Shi, Zuchao Li, Lefei Zhang +3
Large language models (LLMs) based on Transformer Decoders have become the preferred choice for conversational generative AI. Despite the overall superiority of the Decoder archite…
SpindleKV: A Novel KV Cache Reduction Method Balancing Both Shallow and Deep Layers
Zicong Tang, Shi Luohe, Zuchao Li +4
Large Language Models (LLMs) have achieved impressive accomplishments in recent years. However, the increasing memory consumption of KV cache has possessed a significant challenge…
Segment First or Comprehend First? Explore the Limit of Unsupervised Word Segmentation with Large Language Models
Zihong Zhang, Liqi He, Zuchao Li +3
Word segmentation stands as a cornerstone of Natural Language Processing (NLP). Based on the concept of "comprehend first, segment later", we propose a new framework to explore the…
How Deep is Love in LLMs' Hearts? Exploring Semantic Size in Human-like Cognition
Yao Yao, Yifei Yang, Xinbei Ma +4
How human cognitive abilities are formed has long captivated researchers. However, a significant challenge lies in developing meaningful methods to measure these complex processes.…