6 papers
Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning
Yao Fu, Chunxia Zhang, Junmin Liu +3
Generalization remains a pivotal challenge in deep learning, where traditional optimizers like Stochastic Gradient Descent (SGD) often converge to sharp minima, leading to overfitt…
Data Selection for LLM Alignment Using Fine-Grained Preferences
Jia Zhang, Yao Liu, Chen-Xi Zhang +4
Large language models (LLMs) alignment aims to ensure that the behavior of LLMs meets human preferences. While collecting data from multiple fine-grained, aspect-specific preferenc…
Zero-Order Sharpness-Aware Minimization
Yao Fu, Yihang Jin, Chunxia Zhang +3
Prompt learning has become a key method for adapting large language models to specific tasks with limited data. However, traditional gradient-based optimization methods for tuning…
D3: Diversity, Difficulty, and Dependability-Aware Data Selection for Sample-Efficient LLM Instruction Tuning
Jia Zhang, Chen-Xi Zhang, Yao Liu +5
Recent advancements in instruction tuning for large language models (LLMs) suggest that a small, high-quality dataset can significantly equip LLMs with instruction-following capabi…
LawGPT: Knowledge-Guided Data Generation and Its Application to Legal LLM
Zhi Zhou, Kun-Yang Yu, Shi-Yu Tian +6
Large language models (LLMs), both proprietary and open-source, have demonstrated remarkable capabilities across various natural language processing tasks. However, they face signi…
LawGPT: A Chinese Legal Knowledge-Enhanced Large Language Model
Zhi Zhou, Jiang-Xin Shi, Peng-Xiao Song +4
Large language models (LLMs), including both proprietary and open-source models, have showcased remarkable capabilities in addressing a wide range of downstream tasks. Nonetheless,…