40 citations · 71 across the 23 of their papers we have counts for
23 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…
What Kinds of Tokens Benefit from Distant Text? An Analysis on Long Context Language Modeling
Yutong Hu, Quzhe Huang, Kangcheng Luo +1
As the context length that large language models can handle continues to increase, these models demonstrate an enhanced ability to utilize distant information for tasks such as lan…
Can Perplexity Reflect Large Language Model's Ability in Long Text Understanding?
Yutong Hu, Quzhe Huang, Mingxu Tao +2
Recent studies have shown that Large Language Models (LLMs) have the potential to process extremely long text. Many works only evaluate LLMs' long-text processing ability on the la…
Harder Tasks Need More Experts: Dynamic Routing in MoE Models
Quzhe Huang, Zhenwei An, Nan Zhuang +7
In this paper, we introduce a novel dynamic expert selection framework for Mixture of Experts (MoE) models, aiming to enhance computational efficiency and model performance by adju…
Probing Multimodal Large Language Models for Global and Local Semantic Representations
Mingxu Tao, Quzhe Huang, Kun Xu +3
The advancement of Multimodal Large Language Models (MLLMs) has greatly accelerated the development of applications in understanding integrated texts and images. Recent works lever…
Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization
Yang Jin, Zhicheng Sun, Kun Xu +9
In light of recent advances in multimodal Large Language Models (LLMs), there is increasing attention to scaling them from image-text data to more informative real-world videos. Co…
Relation-Aware Question Answering for Heterogeneous Knowledge Graphs
Haowei Du, Quzhe Huang, Chen Li +3
Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer entity in a knowledge graph (KG), which requires multiple steps of reasoning. Existing retrieval-based app…
MC: Towards Transparent and Culturally-Aware NLP for Minority Languages in China
Chen Zhang, Mingxu Tao, Quzhe Huang +3
Current large language models demonstrate deficiencies in understanding low-resource languages, particularly the minority languages in China. This limitation stems from the scarcit…
From Simple to Complex: A Progressive Framework for Document-level Informative Argument Extraction
Quzhe Huang, Yanxi Zhang, Dongyan Zhao
Document-level Event Argument Extraction (EAE) requires the model to extract arguments of multiple events from a single document. Considering the underlying dependencies between th…
Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization
Yang Jin, Kun Xu, Liwei Chen +12
Recently, the remarkable advance of the Large Language Model (LLM) has inspired researchers to transfer its extraordinary reasoning capability to both vision and language data. How…
More than Classification: A Unified Framework for Event Temporal Relation Extraction
Quzhe Huang, Yutong Hu, Shengqi Zhu +3
Event temporal relation extraction~(ETRE) is usually formulated as a multi-label classification task, where each type of relation is simply treated as a one-hot label. This formula…
Lawyer LLaMA Technical Report
Quzhe Huang, Mingxu Tao, Chen Zhang +5
Large Language Models (LLMs), like LLaMA, have exhibited remarkable performance across various tasks. Nevertheless, when deployed to specific domains such as law or medicine, the m…
Do Charge Prediction Models Learn Legal Theory?
Zhenwei An, Quzhe Huang, Cong Jiang +2
The charge prediction task aims to predict the charge for a case given its fact description. Recent models have already achieved impressive accuracy in this task, however, little i…
Knowledge-enhanced Iterative Instruction Generation and Reasoning for Knowledge Base Question Answering
Haowei Du, Quzhe Huang, Chen Zhang +1
Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer entity in a knowledge base which is several hops from the topic entity mentioned in the question. Existing…
Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED
Quzhe Huang, Shibo Hao, Yuan Ye +3
DocRED is a widely used dataset for document-level relation extraction. In the large-scale annotation, a \textit{recommend-revise} scheme is adopted to reduce the workload. Within…
Exploring Distantly-Labeled Rationales in Neural Network Models
Quzhe Huang, Shengqi Zhu, Yansong Feng +1
Recent studies strive to incorporate various human rationales into neural networks to improve model performance, but few pay attention to the quality of the rationales. Most existi…
Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction
Quzhe Huang, Shengqi Zhu, Yansong Feng +3
Document-level Relation Extraction (RE) is a more challenging task than sentence RE as it often requires reasoning over multiple sentences. Yet, human annotators usually use a smal…
Why Machine Reading Comprehension Models Learn Shortcuts?
Yuxuan Lai, Chen Zhang, Yansong Feng +2
Recent studies report that many machine reading comprehension (MRC) models can perform closely to or even better than humans on benchmark datasets. However, existing works indicate…