5 papers
Hypothesis-Disciplined Multi-Agent Automated Formalization of Asymptotic Statistical Theory
Tingzhou Wei, Zeyu Zheng, Ethan X. Fang +1
Asymptotic statistical theory is a challenging domain for AI-assisted formalization: its central results mix convergence statements, asymptotic expansions, functional analysis, and…
Contextual Online Uncertainty-Aware Preference Learning for Human Feedback
Nan Lu, Ethan Lee, Ethan X. Fang +1
Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm in artificial intelligence to align large models with human preferences. In this paper, we propose a…
Predicting Antibiotic Resistance Patterns Using Sentence-BERT: A Machine Learning Approach
Mahmoud Alwakeel, Michael E. Yarrington, Rebekah H. Wrenn +4
Antibiotic resistance poses a significant threat in in-patient settings with high mortality. Using MIMIC-III data, we generated Sentence-BERT embeddings from clinical notes and app…
Fisher Random Walk: Automatic Debiasing Contextual Preference Inference for Large Language Model Evaluation
Yichi Zhang, Alexander Belloni, Ethan X. Fang +2
Motivated by the need for rigorous and scalable evaluation of large language models, we study contextual preference inference for pairwise comparison functionals of context-depende…
Confidence Diagram of Nonparametric Ranking for Uncertainty Assessment in Large Language Models Evaluation
Zebin Wang, Yi Han, Ethan X. Fang +2
We consider the inference for the ranking of large language models (LLMs). Alignment arises as a significant challenge to mitigate hallucinations in the use of LLMs. Ranking LLMs h…