1.3k citations · 1.7k across the 26 of their papers we have counts for
26 papers
APEX: Automated Prompt Engineering eXpert with Dynamic Data Selection
Fei Wang, Si Si, Cho-Jui Hsieh +1
Large Language Models are highly sensitive to prompt formulation, necessitating automatic prompt optimization to unlock their full potential. While evolutionary algorithms have eme…
GroupDPO: Memory-Efficient Group-Wise Direct Preference Optimization
Jixuan Leng, Si Si, Hsiang-Fu Yu +2
Preference optimization is widely used to align Large Language Models (LLMs) with preference feedback. However, most existing methods train on a single positive-negative pair per p…
LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization
Jui-Nan Yen, Si Si, Zhao Meng +5
Low-rank adaption (LoRA) is a widely used parameter-efficient finetuning method for LLM that reduces memory requirements. However, current LoRA optimizers lack transformation invar…
Large Language Models are Interpretable Learners
Ruochen Wang, Si Si, Felix Yu +3
The trade-off between expressiveness and interpretability remains a core challenge when building human-centric predictive models for classification and decision-making. While symbo…
Automatic Engineering of Long Prompts
Cho-Jui Hsieh, Si Si, Felix X. Yu +1
Large language models (LLMs) have demonstrated remarkable capabilities in solving complex open-domain tasks, guided by comprehensive instructions and demonstrations provided in the…
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory
Justin Cui, Ruochen Wang, Si Si +1
Dataset Distillation is a newly emerging area that aims to distill large datasets into much smaller and highly informative synthetic ones to accelerate training and reduce storage.…