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VOILA! Debunking LLMs Exhibiting the Known Sources of Possible Weaknesses: Hallucination, Fairness, Reliability

VOILA! Debunking LLMs Exhibiting the Known Sources of Possible Weaknesses: Hallucination, Fairness, Reliability

VOILA! Debunking LLMs Exhibiting the Known Sources of Possible Weaknesses: Hallucination, Fairness, Reliability
Title

VOILA! Debunking LLMs Exhibiting the Known Sources of Possible Weaknesses: Hallucination, Fairness, Reliability

Lecturer

Frédéric Precioso (Université Côte d'Azur), Frederic.PRECIOSO@univ-cotedazur.fr

Content and organization

Prof. Frédéric Precioso, Université Côte d’Azur

Abstract : By presenting the key principles of LLMs, we will expose how core mechanisms are finally not so complex and how they allow many failures to arise. We will present some of the most recent fancy techniques, such as self-distillation and Reinforcement Learning from Verifiable Reward (RLVR), and how they do not really solve the known weaknesses but potentially open new fields of application and pave the way to apply LLMs efficiently. We then provide insights on evaluation approaches and the persistent limited reliability of the models. We finally expose the ethical implication of such technical choices, presenting the studies of AI biases and fairness.

Speaker’s BIO: Frédéric Precioso is a Professor of Computer Science at Université Côte d’Azur. He has made methodological contributions to areas of artificial intelligence, machine learning, and deep learning, such as explainability, hybridization of symbolic and non-symbolic AI, knowledge injection, low-data domains of expertise (medicine, social sciences). Professor Precioso was appointed to the French National Research Agency (ANR) where, from 2018 to 2023, he was in charge of programs associated with the National AI Plan within the Directorate of Major State Investment Projects and the Department of Digital Sciences and Mathematics. He was responsible for all ANR funding instruments related to the French artificial intelligence strategy.

Course Duration

1,5

Course Type

Short Course

Participation terms

Attendance is free and open to everyone interested. Please register via the link above, and you will receive the Zoom meeting details one day before the seminar.

Modality (online/in person):

Online

Course Link

https://voila-seminars.tilda.ws/tpost/s4e4-ai-ethics-bias

Host Institution
Université Côte d'Azur

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