1 citations · 1 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
How Contaminated Is Your Benchmark? Quantifying Dataset Leakage in Large Language Models with Kernel Divergence
Hyeong Kyu Choi, Maxim Khanov, Hongxin Wei +1
Dataset contamination, where evaluation datasets overlap with pre-training corpora, inflates performance metrics and undermines the reliability of model evaluations. Measuring data…