5 papers
Socio-Foundation: A Model for Generalizable Individual Behavior Simulation via Hierarchical Capability Distillation
Liang Wang, Wenxuan Xie, Xinyi Mou +2
Simulating individual behavior requires large language models (LLMs) to preserve persona traits while adapting to dynamic social contexts. However, general-purpose LLMs often flatt…
Beyond Isolated Behaviors: Hierarchical User Modeling for LLM Personalization
Liang Wang, Xinyi Mou, Xiaoyou Liu +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, yet personalizing their outputs to individual users remains an open challenge. Existi…
CURP: Codebook-based Continuous User Representation for Personalized Generation with LLMs
Liang Wang, Xinyi Mou, Xiaoyou Liu +2
User modeling characterizes individuals through their preferences and behavioral patterns to enable personalized simulation and generation with Large Language Models (LLMs) in cont…
PersonaDual: Balancing Personalization and Objectivity via Adaptive Reasoning
Xiaoyou Liu, Xinyi Mou, Shengbin Yue +5
As users increasingly expect LLMs to align with their preferences, personalized information becomes valuable. However, personalized information can be a double-edged sword: it can…
From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents
Xinyi Mou, Xuanwen Ding, Qi He +8
Traditional sociological research often relies on human participation, which, though effective, is expensive, challenging to scale, and with ethical concerns. Recent advancements i…