6 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…
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…
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…
D.Va: Validate Your Demonstration First Before You Use It
Qi Zhang, Zhiqing Xiao, Ruixuan Xiao +2
In-context learning (ICL) has demonstrated significant potential in enhancing the capabilities of large language models (LLMs) during inference. It's well-established that ICL heav…
AIGT: AI Generative Table Based on Prompt
Mingming Zhang, Zhiqing Xiao, Guoshan Lu +5
Tabular data, which accounts for over 80% of enterprise data assets, is vital in various fields. With growing concerns about privacy protection and data-sharing restrictions, gener…