9 citations · 20 across the 8 of their papers we have counts for
4 papers · 1 filter
MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning
Jingfan Zhang, Yi Zhao, Dan Chen +3
Low-rank adaptation (LoRA) and its mixture-of-experts (MOE) variants are highly effective parameter-efficient fine-tuning (PEFT) methods. However, they introduce significant latenc…
TCMBench: A Comprehensive Benchmark for Evaluating Large Language Models in Traditional Chinese Medicine
Wenjing Yue, Xiaoling Wang, Wei Zhu +5
Large language models (LLMs) have performed remarkably well in various natural language processing tasks by benchmarking, including in the Western medical domain. However, the prof…
PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain
Wei Zhu, Xiaoling Wang, Huanran Zheng +2
Biomedical language understanding benchmarks are the driving forces for artificial intelligence applications with large language model (LLM) back-ends. However, most current benchm…
Candidate Soups: Fusing Candidate Results Improves Translation Quality for Non-Autoregressive Translation
Huanran Zheng, Wei Zhu, Pengfei Wang +1
Non-autoregressive translation (NAT) model achieves a much faster inference speed than the autoregressive translation (AT) model because it can simultaneously predict all tokens du…