6 papers
LLJ Cards: Best practices for the Use of LLMs as Judges
Khaoula Chehbouni, Melina Medjdoub, Florian Carichon +2
In recent years, large language models (LLMs) have emerged as a popular alternative for evaluation. Often referred to as LLMs as judges (LLJs), these systems have been widely adopt…
Neither Valid nor Reliable? Investigating the Use of LLMs as Judges
Khaoula Chehbouni, Mohammed Haddou, Jackie Chi Kit Cheung +1
Evaluating natural language generation (NLG) systems remains a core challenge of natural language processing (NLP), further complicated by the rise of large language models (LLMs)…
Fairness in Federated Learning: Fairness for Whom?
Afaf Taik, Khaoula Chehbouni, Golnoosh Farnadi
Fairness in federated learning has emerged as a rapidly growing area of research, with numerous works proposing formal definitions and algorithmic interventions. Yet, despite this…
Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy
Khaoula Chehbouni, Martine De Cock, Gilles Caporossi +3
The increased screen time and isolation caused by the COVID-19 pandemic have led to a significant surge in cases of online grooming, which is the use of strategies by predators to…
Beyond the Safety Bundle: Auditing the Helpful and Harmless Dataset
Khaoula Chehbouni, Jonathan Colaço Carr, Yash More +2
In an effort to mitigate the harms of large language models (LLMs), learning from human feedback (LHF) has been used to steer LLMs towards outputs that are intended to be both less…
From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards
Khaoula Chehbouni, Megha Roshan, Emmanuel Ma +4
Recent progress in large language models (LLMs) has led to their widespread adoption in various domains. However, these advancements have also introduced additional safety risks an…