7 citations · 7 across the 6 of their papers we have counts for
9 papers
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…
KANLab: TensorFlow Playground for Kolmogorov-Arnold Networks
Lukas Niehaus, Robin Rawiel, Ulf Krumnack
KANLab is a browser-based visualization tool designed to interactively teach the core concepts of Kolmogorov-Arnold Networks (KAN), forked from the widely successful TensorFlow Pla…
SPICE: Submodular Penalized Information-Conflict Selection for Efficient Large Language Model Training
Powei Chang, Jinpeng Zhang, Bowen Chen +9
Information-based data selection for instruction tuning is compelling: maximizing the log-determinant of the Fisher information yields a monotone submodular objective, enabling gre…
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…
Packing Analysis: Packing Is More Appropriate for Large Models or Datasets in Supervised Fine-tuning
Shuhe Wang, Guoyin Wang, Yizhong Wang +3
Packing, initially utilized in the pre-training phase, is an optimization technique designed to maximize hardware resource efficiency by combining different training sequences to f…
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…
A Survey on Data Augmentation in Large Model Era
Yue Zhou, Chenlu Guo, Xu Wang +2
Large models, encompassing large language and diffusion models, have shown exceptional promise in approximating human-level intelligence, garnering significant interest from both a…