How can cognitive models improve LLM-based prediction of user engagement?
Improving the prediction of individual engagement in recommendations using cognitive models
August 30, 2024
https://arxiv.org/pdf/2408.16147This paper investigates how cognitive models, specifically Instance-Based Learning Theory (IBLT), can improve engagement prediction in multi-agent AI systems for healthcare. Instead of relying solely on traditional time-series forecasters like LSTMs, the authors propose using personalized IBL models to capture individual beneficiary behavior dynamics.
The key takeaway for LLM-based multi-agent systems is the potential of incorporating IBL models to represent individual agents. This approach could lead to more accurate predictions of agent behavior by accounting for individual history, memory effects, and context similarity, ultimately leading to more effective interventions in multi-agent applications.