1 citations · 1 across the 10 of their papers we have counts for
11 papers
From Parameters to Data: A Task-Parameter-Guided Fine-Tuning Pipeline for Efficient LLM Alignment
Hao Chen, Qi Zhang, Liyao Li +7
Adapting Large Language Models (LLMs) to specialized domains typically incurs high data and computational overhead. While prior efficiency efforts have largely treated data selecti…
TabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding
Minjie Qiang, Mingming Zhang, Xiaoyi Bao +5
Foundation models have established unified representations for natural language processing, yet this paradigm remains largely unexplored for tabular data. Existing methods face fun…
KMLP: A Scalable Hybrid Architecture for Web-Scale Tabular Data Modeling
Mingming Zhang, Pengfei Shi, Zhiqing Xiao +8
Predictive modeling on web-scale tabular data with billions of instances and hundreds of heterogeneous numerical features faces significant scalability challenges. These features e…
Table as a Modality for Large Language Models
Liyao Li, Chao Ye, Wentao Ye +9
To migrate the remarkable successes of Large Language Models (LLMs), the community has made numerous efforts to generalize them to the table reasoning tasks for the widely deployed…
Chinese ModernBERT with Whole-Word Masking
Zeyu Zhao, Ningtao Wang, Xing Fu +1
Encoder-only Transformers have advanced along three axes -- architecture, data, and systems -- yielding Pareto gains in accuracy, speed, and memory efficiency. Yet these improvemen…
Improved Personalized Headline Generation via Denoising Fake Interests from Implicit Feedback
Kejin Liu, Junhong Lian, Xiang Ao +5
Accurate personalized headline generation hinges on precisely capturing user interests from historical behaviors. However, existing methods neglect personalized-irrelevant click no…
ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models
Hao Chen, Haoze Li, Zhiqing Xiao +6
Aligning general-purpose large language models (LLMs) to downstream tasks often incurs significant training adjustment costs. Prior research has explored various avenues to enhance…
Beyond Tree Models: A Hybrid Model of KAN and gMLP for Large-Scale Financial Tabular Data
Mingming Zhang, Jiahao Hu, Pengfei Shi +8
Tabular data plays a critical role in real-world financial scenarios. Traditionally, tree models have dominated in handling tabular data. However, financial datasets in the industr…
Ultra-imbalanced classification guided by statistical information
Yin Jin, Ningtao Wang, Ruofan Wu +3
Imbalanced data are frequently encountered in real-world classification tasks. Previous works on imbalanced learning mostly focused on learning with a minority class of few samples…
Estimating Conditional Average Treatment Effects via Sufficient Representation Learning
Pengfei Shi, Wei Zhong, Xinyu Zhang +4
Estimating the conditional average treatment effects (CATE) is very important in causal inference and has a wide range of applications across many fields. In the estimation process…
Self-supervision meets kernel graph neural models: From architecture to augmentations
Jiawang Dan, Ruofan Wu, Yunpeng Liu +8
Graph representation learning has now become the de facto standard when handling graph-structured data, with the framework of message-passing graph neural networks (MPNN) being the…