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Zhexiao Lin

2 accepted papers

2026

Domain-Shift-Aware Conformal Prediction for Large Language Models

ICML 2026poster

Large language models have achieved impressive performance across diverse tasks. However, their tendency to produce overconfident and factually incorrect outputs, known as hallucinations, poses risks in real world applications. Conformal prediction provides finite-sample, distribution-free coverage …

Cited by 0SourceScholar
2025

BBScoreV2: Learning Time-Evolution and Latent Alignment from Stochastic Representation

EMNLP 2025

Autoregressive generative models play a key role in various language tasks, especially for modeling and evaluating long text sequences. While recent methods leverage stochastic representations to better capture sequence dynamics, encoding both temporal and structural dependencies and utilizing such