5 citations · 13 across the 13 of their papers we have counts for
21 papers
Can Language Models Discover Scaling Laws?
Haowei Lin, Haotian Ye, Wenzheng Feng +8
Discovering scaling laws for predicting model performance at scale is a fundamental and open-ended challenge, mostly reliant on slow, case specific human experimentation. To invest…
JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning
Huanghai Liu, Quzhe Huang, Qingjing Chen +5
In recent years, Large Language Models (LLMs) have been widely applied to legal tasks. To enhance their understanding of legal texts and improve reasoning accuracy, a promising app…
Automating Legal Interpretation with LLMs: Retrieval, Generation, and Evaluation
Kangcheng Luo, Quzhe Huang, Cong Jiang +1
Interpreting the law is always essential for the law to adapt to the ever-changing society. It is a critical and challenging task even for legal practitioners, as it requires metic…
Pyramidal Flow Matching for Efficient Video Generative Modeling
Yang Jin, Zhicheng Sun, Ningyuan Li +7
Video generation requires modeling a vast spatiotemporal space, which demands significant computational resources and data usage. To reduce the complexity, the prevailing approache…
Only One Relation Possible? Modeling the Ambiguity in Event Temporal Relation Extraction
Yutong Hu, Quzhe Huang, Yansong Feng
Event Temporal Relation Extraction (ETRE) aims to identify the temporal relationship between two events, which plays an important role in natural language understanding. Most previ…
Unlocking the Potential of Model Merging for Low-Resource Languages
Mingxu Tao, Chen Zhang, Quzhe Huang +4
Adapting large language models (LLMs) to new languages typically involves continual pre-training (CT) followed by supervised fine-tuning (SFT). However, this CT-then-SFT approach s…