4 papers
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…
QSpec: Speculative Decoding with Complementary Quantization Schemes
Juntao Zhao, Wenhao Lu, Sheng Wang +2
Quantization is widely adopted to accelerate inference and reduce memory consumption in large language models (LLMs). While activation-weight joint quantization enables efficient l…
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…