7 citations · 7 across the 6 of their papers we have counts for
5 papers · 1 filter
PremiseSQL: Evaluating Premise Critique and Abstention in Text-to-SQL Systems
Shuo Lin, Jinzhe Li, Chenlu Guo +3
Recent advances in large language models (LLMs) have substantially improved Text-to-SQL performance, yet existing NL2SQL systems largely assume that user queries are valid and answ…
CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing
Zewen Jiang, Yahan Li, Chenlu Guo +3
Large language models (LLMs) have demonstrated multilingual capabilities across diverse language generation and understanding tasks. Yet effective code-switching requires more than…
NLoRA: Nyström-Initiated Low-Rank Adaptation for Large Language Models
Chenlu Guo, Yuan Wu, Yi Chang
Parameter-efficient fine-tuning (PEFT) is essential for adapting large language models (LLMs), with low-rank adaptation (LoRA) being the most popular approach. However, LoRA suffer…
LoRA-MGPO: Mitigating Double Descent in Low-Rank Adaptation via Momentum-Guided Perturbation Optimization
Yupeng Chang, Chenlu Guo, Yi Chang +1
Parameter-efficient fine-tuning (PEFT), particularly Low-Rank Adaptation (LoRA), adapts large language models (LLMs) by training only a small fraction of parameters. However, as th…
CHBench: A Chinese Dataset for Evaluating Health in Large Language Models
Chenlu Guo, Nuo Xu, Yi Chang +1
With the rapid development of large language models (LLMs), assessing their performance on health-related inquiries has become increasingly essential. The use of these models in re…