2 citations · 3 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…