1 citations · 1 across the 1 of their papers we have counts for
5 papers
Verify when Uncertain: Beyond Self-Consistency in Black Box Hallucination Detection
Yihao Xue, Kristjan Greenewald, Youssef Mroueh +1
Large Language Models (LLMs) often hallucinate, limiting their reliability in sensitive applications. In black-box settings, several self-consistency-based techniques have been pro…
Beyond What Seems Necessary: Hidden Gains from Scaling Training-Time Reasoning Length under Outcome Supervision
Yihao Xue, Allan Zhang, Jianhao Huang +2
Training LLMs to think and reason for longer has become a key ingredient in building state-of-the-art models that can solve complex problems previously out of reach. Recent efforts…
LoRA is All You Need for Safety Alignment of Reasoning LLMs
Yihao Xue, Baharan Mirzasoleiman
Reasoning-capable LLMs have achieved major breakthroughs in solving complex problems, but recent work shows that acquiring and deploying strong reasoning can introduce significant…
Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions
Yihao Xue, Jiping Li, Baharan Mirzasoleiman
Weak-to-Strong Generalization (W2SG), where a weak model supervises a stronger one, serves as an important analogy for understanding how humans might guide superhuman intelligence…
Challenges and Opportunities in Improving Worst-Group Generalization in Presence of Spurious Features
Siddharth Joshi, Yu Yang, Yihao Xue +2
Deep neural networks often exploit *spurious* features that are present in the majority of examples within a class during training. This leads to *poor worst-group test accuracy*,…