PF
Pascale Fung
CRIO & Co-Founder
ParisCRIOAMI Labs
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Biography

Pascale Fung is a pioneering researcher and technologist in the field of human-centered artificial intelligence, whose decades of foundational work in natural language processing, speech recognition, and machine intelligence have established her as one of the most influential figures in the global AI research community.

She holds a Doctor of Philosophy degree from Columbia University, where she developed early expertise in the computational and statistical approaches to language understanding that would define much of her subsequent career. Her academic training laid the groundwork for a research trajectory spanning both fundamental science and applied machine intelligence.

Fung served as a Chair Professor at the Hong Kong University of Science and Technology (HKUST), where she led research into spoken language processing, multilingual AI systems, and empathetic computing. Her work at HKUST encompassed natural language processing, human-machine communication, and the development of AI systems capable of understanding and responding to human emotion — a body of research that helped shape the emerging field of affective computing and conversational AI.

She is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), the International Speech Communication Association (ISCA), the Association for Computational Linguistics (ACL), and the Association for the Advancement of Artificial Intelligence (AAAI), reflecting the breadth of her contributions across multiple scientific disciplines.\n\nPrior to co-founding AMI Labs, Fung served as Senior Director at Meta's Fundamental AI Research (FAIR) laboratory, one of the world's foremost industrial AI research organizations. In that capacity, she helped advance large-scale AI research at the frontier of the field, working alongside leading scientists on foundational challenges in machine learning and artificial intelligence. Her tenure at Meta FAIR further cemented her standing as a bridge between academic research and real-world AI development at scale.

Career History
2022–2024
Meta AI (FAIR)
Senior Director of Research
1993–present
HKUST
Chair Professor of AI, ECE & CS
1989–1994
Columbia University
PhD, Computer Science
Key Papers
Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation, and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions, and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, and machine translation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.
2023 · ACM Comput. Surv.
7,622 citations