2 citations · 3 across the 9 of their papers we have counts for
11 papers
NADS: Navigator-Guided Data Selection for Mitigating Catastrophic Forgetting in Fine-Tuning
Jianhao Zhang, Ou Wu, Yi Du
Catastrophic forgetting is a central obstacle in single-stage fine-tuning of large language models (LLMs) on a new task, especially when the original pretraining data are unavailab…
Token Utility Is Selection-Conditioned: Coupled Selection of Prompt Context and Response Supervision for Instruction Tuning
Can Wu, Xinrui Chen, Ou Wu +1
Token selection for instruction tuning is coupled: supervision utility depends on retained context, and vice versa. Existing methods largely optimize prompt compression or response…
CITE: A Comprehensive Benchmark for Heterogeneous Text-Attributed Graphs on Catalytic Materials
Chenghao Zhang, Qingqing Long, Ludi Wang +3
Text-attributed graphs(TAGs) are pervasive in real-world systems,where each node carries its own textual features. In many cases these graphs are inherently heterogeneous, containi…
Disentangled Multi-span Evolutionary Network against Temporal Knowledge Graph Reasoning
Hao Dong, Ziyue Qiao, Zhiyuan Ning +4
Temporal Knowledge Graphs (TKGs), as an extension of static Knowledge Graphs (KGs), incorporate the temporal feature to express the transience of knowledge by describing when facts…
Distilling Closed-Source LLM's Knowledge for Locally Stable and Economic Biomedical Entity Linking
Yihao Ai, Zhiyuan Ning, Weiwei Dai +5
Biomedical entity linking aims to map nonstandard entities to standard entities in a knowledge base. Traditional supervised methods perform well but require extensive annotated dat…
Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization
Xiaohan Huang, Dongjie Wang, Zhiyuan Ning +7
Feature transformation methods aim to find an optimal mathematical feature-feature crossing process that generates high-value features and improves the performance of downstream ma…
Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning
Weiliang Zhang, Xiaohan Huang, Yi Du +5
Feature selection aims to preprocess the target dataset, find an optimal and most streamlined feature subset, and enhance the downstream machine learning task. Among filter, wrappe…
FastFT: Accelerating Reinforced Feature Transformation via Advanced Exploration Strategies
Tianqi He, Xiaohan Huang, Yi Du +6
Feature Transformation is crucial for classic machine learning that aims to generate feature combinations to enhance the performance of downstream tasks from a data-centric perspec…
FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data
Haoran Sun, Renren Jin, Shaoyang Xu +10
Large language models (LLMs) have demonstrated prowess in a wide range of tasks. However, many LLMs exhibit significant performance discrepancies between high- and low-resource lan…
M3GIA: A Cognition Inspired Multilingual and Multimodal General Intelligence Ability Benchmark
Wei Song, Yadong Li, Jianhua Xu +8
As recent multi-modality large language models (MLLMs) have shown formidable proficiency on various complex tasks, there has been increasing attention on debating whether these mod…
CataLM: Empowering Catalyst Design Through Large Language Models
Ludi Wang, Xueqing Chen, Yi Du +3
The field of catalysis holds paramount importance in shaping the trajectory of sustainable development, prompting intensive research efforts to leverage artificial intelligence (AI…