8 papers
Detecting Distillation Data from Reasoning Models
Hengxiang Zhang, Hyeong Kyu Choi, Sharon Li +1
Reasoning distillation has emerged as a prevailing paradigm for transferring reasoning capabilities from large reasoning models to small language models. Yet, reasoning distillatio…
When Identity Skews Debate: Anonymization for Bias-Reduced Multi-Agent Reasoning
Hyeong Kyu Choi, Xiaojin Zhu, Sharon Li
Multi-agent debate (MAD) aims to improve large language model (LLM) reasoning by letting multiple agents exchange answers and then aggregate their opinions. Yet recent studies reve…
Beyond In-Domain Detection: SpikeScore for Cross-Domain Hallucination Detection
Yongxin Deng, Zhen Fang, Sharon Li +1
Hallucination detection is critical for deploying large language models (LLMs) in real-world applications. Existing hallucination detection methods achieve strong performance when…
Your Pre-trained LLM is Secretly an Unsupervised Confidence Calibrator
Beier Luo, Shuoyuan Wang, Sharon Li +1
Post-training of large language models is essential for adapting pre-trained language models (PLMs) to align with human preferences and downstream tasks. While PLMs typically exhib…
Debate or Vote: Which Yields Better Decisions in Multi-Agent Large Language Models?
Hyeong Kyu Choi, Xiaojin Zhu, Sharon Li
Multi-Agent Debate~(MAD) has emerged as a promising paradigm for improving the performance of large language models through collaborative reasoning. Despite recent advances, the ke…
Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models
Shuoyuan Wang, Yixuan Li, Hongxin Wei
Confidence calibration is critical for the safe deployment of machine learning models in the real world. However, such issue in vision-language models like CLIP, particularly after…