1 citations · 2 across the 5 of their papers we have counts for
7 papers
CONDUIT: A Unified Residual-Stream Restoration Framework for KV Cache Reuse in Vision-Language Models
Pengan Chen, Kaisheng Zheng, Liang Hong +10
Vision-language models (VLMs) often answer new questions about recurring visual content, where reusing the key-value (KV) cache can avoid re-encoding expensive visual prefixes. Exa…
Benchmarking Large Language Models on Multiple Tasks in Bioinformatics NLP with Prompting
Jiyue Jiang, Pengan Chen, Jiuming Wang +13
Large language models (LLMs) have become important tools in solving biological problems, offering improvements in accuracy and adaptability over conventional methods. Several bench…
Developing and Utilizing a Large-Scale Cantonese Dataset for Multi-Tasking in Large Language Models
Jiyue Jiang, Alfred Kar Yin Truong, Yanyu Chen +7
High-quality data resources play a crucial role in learning large language models (LLMs), particularly for low-resource languages like Cantonese. Despite having more than 85 millio…
TreeSynth: Synthesizing Diverse Data from Scratch via Tree-Guided Subspace Partitioning
Sheng Wang, Pengan Chen, Jingqi Zhou +7
Model customization necessitates high-quality and diverse datasets, but acquiring such data remains time-consuming and labor-intensive. Despite the great potential of large languag…
Large Language Models in Bioinformatics: A Survey
Zhenyu Wang, Zikang Wang, Jiyue Jiang +3
Large Language Models (LLMs) are revolutionizing bioinformatics, enabling advanced analysis of DNA, RNA, proteins, and single-cell data. This survey provides a systematic review of…
MoS: Unleashing Parameter Efficiency of Low-Rank Adaptation with Mixture of Shards
Sheng Wang, Liheng Chen, Pengan Chen +5
The rapid scaling of large language models necessitates more lightweight finetuning methods to reduce the explosive GPU memory overhead when numerous customized models are served s…
How Well Do LLMs Handle Cantonese? Benchmarking Cantonese Capabilities of Large Language Models
Jiyue Jiang, Pengan Chen, Liheng Chen +5
The rapid evolution of large language models (LLMs) has transformed the competitive landscape in natural language processing (NLP), particularly for English and other data-rich lan…