activity
20242026
most citedImproved Personalized Headline Generation via Denoising Fake Interests from Implicit Feedback

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CL2026

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…

cs.CL2025

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…

cs.CL20251 cited

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…

cs.CL2025

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…

cs.LG2024

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…

cs.LG2024

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…