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Kevin Zhou

2 accepted papers

2025

Evaluating Uncertainty Quantification Methods in Argumentative Large Language Models

EMNLP 2025

Research in uncertainty quantification (UQ) for large language models (LLMs) is increasingly important towards guaranteeing the reliability of this groundbreaking technology. We explore the integration of LLM UQ methods in argumentative LLMs (ArgLLMs), an explainable LLM framework for decision-makin

Cited by 0SourcePDFScholar
2023

The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning

NeurIPS 2023poster

While distributional reinforcement learning (DistRL) has been empirically effective, the question of when and why it is better than vanilla, non-distributional RL has remained unanswered. This paper explains the benefits of DistRL through the lens of small-loss bounds, which are instance-dependent b…