Advancements in LLM-Driven Behavioral Modeling

The field of Large Language Models (LLMs) is moving towards more human-aligned and embodied agents, with a focus on incorporating intrinsic motivation and physical grounding into their design. Recent studies have highlighted the gap between the value-driven nature of human cognition and the statistical patterns of LLMs, emphasizing the need for more nuanced and human-like agents. Noteworthy papers in this area include:

  • Mind the Gap: The Divergence Between Human and LLM-Generated Tasks, which explores the disconnect between human task generation and LLM-generated tasks.
  • LLM Agent-Based Simulation of Student Activities and Mental Health Using Smartphone Sensing Data, which proposes a novel framework for modeling student activities and mental health using LLM agents.

Sources

Mind the Gap: The Divergence Between Human and LLM-Generated Tasks

LLM Agent-Based Simulation of Student Activities and Mental Health Using Smartphone Sensing Data

AgentSME for Simulating Diverse Communication Modes in Smart Education

InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation

Simulating Human-Like Learning Dynamics with LLM-Empowered Agents

Built with on top of