4 papers
Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models
Weihao Xuan, Qingcheng Zeng, Heli Qi +2
Uncertainty quantification is essential for assessing the reliability and trustworthiness of modern AI systems. Among existing approaches, verbalized uncertainty, where models expr…
Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models
Qingcheng Zeng, Mingyu Jin, Qinkai Yu +12
Large Language Models (LLMs) are employed across various high-stakes domains, where the reliability of their outputs is crucial. One commonly used method to assess the reliability…
Large Language Models Are Partially Primed in Pronoun Interpretation
Suet-Ying Lam, Qingcheng Zeng, Kexun Zhang +2
While a large body of literature suggests that large language models (LLMs) acquire rich linguistic representations, little is known about whether they adapt to linguistic biases i…
Low-resource Accent Classification in Geographically-proximate Settings: A Forensic and Sociophonetics Perspective
Qingcheng Zeng, Dading Chong, Peilin Zhou +1
Accented speech recognition and accent classification are relatively under-explored research areas in speech technology. Recently, deep learning-based methods and Transformer-based…