4 papers
BiasTrace: Linking Reasoning Behaviours to Biased Outputs in LLMs
Varsha Ramineni, Hossein A. Rahmani, Jerome Ramos +2
LLMs exhibit social biases that can produce inaccurate and discriminatory inferences, posing risks in high-stakes applications. While prior work has made progress in measuring and…
Collaborative User Prompt for Personalized Generative Recommendation
Jerome Ramos, Bin Wu, Aldo Lipani
Large Language Models (LLMs) have become powerful foundations for generative recommender systems, framing recommendation tasks as text generation tasks. However, existing generativ…
Interplay: Training Independent Simulators for Reference-Free Conversational Recommendation
Jerome Ramos, Feng Xia, Xi Wang +4
Training conversational recommender systems (CRS) requires extensive dialogue data, which is challenging to collect at scale. To address this, researchers have used simulated user-…
PREF: Reference-Free Evaluation of Personalised Text Generation in LLMs
Xiao Fu, Hossein A. Rahmani, Bin Wu +3
Personalised text generation is essential for user-centric information systems, yet most evaluation methods overlook the individuality of users. We introduce \textbf{PREF}, a \text…