6 papers
Any Large Language Model Can Be a Reliable Judge: Debiasing with a Reasoning-based Bias Detector
Haoyan Yang, Runxue Bao, Cao Xiao +4
LLM-as-a-Judge has emerged as a promising tool for automatically evaluating generated outputs, but its reliability is often undermined by potential biases in judgment. Existing eff…
Safe Screening Rules for Group SLOPE
Runxue Bao, Quanchao Lu, Yanfu Zhang
Variable selection is a challenging problem in high-dimensional sparse learning, especially when group structures exist. Group SLOPE performs well for the adaptive selection of gro…
Safe Screening Rules for Group OWL Models
Runxue Bao, Quanchao Lu, Yanfu Zhang
Group Ordered Weighted -Norm (Group OWL) regularized models have emerged as a useful procedure for high-dimensional sparse multi-task learning with correlated features. Prox…
Dynamic Uncertainty Ranking: Enhancing Retrieval-Augmented In-Context Learning for Long-Tail Knowledge in LLMs
Shuyang Yu, Runxue Bao, Parminder Bhatia +3
Large language models (LLMs) can learn vast amounts of knowledge from diverse domains during pre-training. However, long-tail knowledge from specialized domains is often scarce and…
All-in-One Tuning and Structural Pruning for Domain-Specific LLMs
Lei Lu, Zhepeng Wang, Runxue Bao +7
Existing pruning techniques for large language models (LLMs) targeting domain-specific applications typically follow a two-stage process: pruning the pretrained general-purpose LLM…
A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases
Zhepeng Wang, Runxue Bao, Yawen Wu +6
Graph neural networks (GNNs) are powerful machine learning models designed to handle irregularly structured data. However, their generic design often proves inadequate for analyzin…