Matching Game Preferences Through Dialogical Large Language Models: A Perspective

« This perspective paper explores the future potential of « conversational intelligence » by examining how Large Language Models (LLMs) could be combined with GRAPHYP’s network system to better understand human conversations and preferences. Using recent research and case studies, we propose a conceptual framework that could make AI rea-soning transparent and traceable, allowing humans to see and understand how AI reaches its conclusions. We present the conceptual perspective of « Matching Game Preferences through Dialogical Large Language Models (D-LLMs), » a proposed system that would allow multiple users to share their different preferences through structured conversations. This approach envisions personalizing LLMs by embedding individual user preferences directly into how the model makes decisions. (…) »

source > arxiv.org, Renaud Fabre, Daniel Egret, Patrice Bellot, arXiv:2507.20000v1, https://doi.org/10.48550/arXiv.2507.20000 Journal reference:Applied Sciences, 2025, 15(15), 8307, https://doi.org/10.3390/app15158307

• Mots-clés : 
• Intelligence artificielle (IA),
• Large Language Models (LLMs)