3 citations · 3 across the 9 of their papers we have counts for
4 papers · 1 filter
AI Alignment Breaks at the Edge
Han Bao, Yue Huang, Xiaoda Wang +5
General Alignment has improved average-case helpfulness and safety, but current alignment practice still rewards confident, single-turn responses. The problem is not only that mode…
ProbeLLM: Automating Principled Diagnosis of LLM Failures
Yue Huang, Zhengzhe Jiang, Yuchen Ma +8
Understanding how and why large language models (LLMs) fail is becoming a central challenge as models rapidly evolve and static evaluations fall behind. While automated probing has…
Better Datasets Start From RefineLab: Automatic Optimization for High-Quality Dataset Refinement
Xiaonan Luo, Yue Huang, Ping He +1
High-quality Question-Answer (QA) datasets are foundational for reliable Large Language Model (LLM) evaluation, yet even expert-crafted datasets exhibit persistent gaps in domain c…
SPA: Achieving Consensus in LLM Alignment via Self-Priority Optimization
Yue Huang, Xiangqi Wang, Xiangliang Zhang
In high-stakes scenarios-such as self-harm, legal, or medical queries-LLMs must be both trustworthy and helpful. However, these goals often conflict. We propose priority alignment,…