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Yuliang Yan

3 accepted papers

2026

How Far Are LLMs from Professional Poker Players? Revisiting Game-Theoretic Reasoning with Agentic Tool Use

ICLR 2026poster

As Large Language Models (LLMs) are increasingly applied in high-stakes domains, their ability to reason strategically under uncertainty becomes critical. Poker provides a rigorous testbed, requiring not only strong actions but also principled, game-theoretic reasoning. In this paper, we conduct a s…

Cited by 0SourceScholar
2023

Hallucination Detection for Generative Large Language Models by Bayesian Sequential Estimation

EMNLP 2023long main

Large Language Models (LLMs) have made remarkable advancements in the field of natural language generation. However, the propensity of LLMs to generate inaccurate or non-factual content, termed "hallucinations", remains a significant challenge. Current hallucination detection methods often necessita…

Cited by 0SourceScholar
2023

Measure Children's Mindreading Ability with Machine Reading

EMNLP 2023long findings

Recently, much research in psychology has benefited from the advances in machine learning techniques. Some recent studies showed that it is possible to build automated scoring models for children's mindreading. These models were trained on a set of manually-labeled question-response pairs, which we…

Cited by 0SourceScholar