8 papers
TLRD: Teaching LLMs to Reason over Tabular Data with Tri-Level Rationale Distillation
Tianyuan Liang, Xuwei Tan, Lei Shi +6
Tabular data is a primary medium for storing real-world information, driving many industrial applications of machine learning. Traditional predictors achieve strong predictive perf…
Understanding Structured Financial Data with LLMs: A Case Study on Fraud Detection
Xuwei Tan, Yao Ma, Xueru Zhang
Detecting fraud in financial transactions typically relies on tabular models that demand heavy feature engineering to handle high-dimensional data and offer limited interpretabilit…
Benchmarking Bias Mitigation Toward Fairness Without Harm from Vision to LVLMs
Xuwei Tan, Ziyu Hu, Xueru Zhang
Machine learning models trained on real-world data often inherit and amplify biases against certain social groups, raising urgent concerns about their deployment at scale. While nu…
DABench-LLM: Standardized and In-Depth Benchmarking of Post-Moore Dataflow AI Accelerators for LLMs
Ziyu Hu, Zhiqing Zhong, Weijian Zheng +6
The exponential growth of large language models has outpaced the capabilities of traditional CPU and GPU architectures due to the slowdown of Moore's Law. Dataflow AI accelerators…
Achieving Fairness Without Harm via Selective Demographic Experts
Xuwei Tan, Yuanlong Wang, Thai-Hoang Pham +2
As machine learning systems become increasingly integrated into human-centered domains such as healthcare, ensuring fairness while maintaining high predictive performance is critic…
ProFL: Performative Robust Optimal Federated Learning
Xue Zheng, Tian Xie, Xuwei Tan +2
Performative prediction is a framework that captures distribution shifts that occur during the training of machine learning models due to their deployment. As the trained model is…