3 papers
cs.CL2026
When the Wrong Key Wins: Understanding and Detecting Hallucinations in LLMs
Xuhan Tong, Jiawei Zhang
Large language models can hallucinate even when the knowledge required for a correct answer is already available. We study this failure through a latent-key view of inference, wher…
cs.CL2026
Understanding Why Language Models Hallucinate: Testing Reasoning Against Priors
Yangfan Hu, Xuhan Tong, Haoyue Bai +5
Large language models often produce hallucinated answers that violate prompt-level constraints. A key diagnostic question is whether these failures reflect missing knowledge, or wh…
cs.LG2026
Demonstrations, CoT, and Prompting: A Theoretical Analysis of ICL
Xuhan Tong, Yuchen Zeng, Jiawei Zhang
In-Context Learning (ICL) enables pretrained LLMs to adapt to downstream tasks by conditioning on a small set of input-output demonstrations, without any parameter updates. Althoug…