1.6k citations · 1.6k across the 13 of their papers we have counts for
7 papers · 1 filter
SCBO: Semantically Coherent Batching and Ordering for LLM-Based Social Surveys
Yuanzi Li, Lingjie Wang, Zihang Tian +2
Large Language Models (LLMs) offer a scalable way to simulate survey respondents using demographic profiles and observed reference responses. However, the conventional approach of…
Adaptive Resource Allocation for Effective and Efficient LLM Social Survey Simulation
Yuanzi Li, Xueyang Feng, Junhao Wang +2
Large Language Models (LLMs) enable scalable social survey simulation, yet existing pipelines typically use the same strong general-purpose model and a fixed, often large, responde…
Understanding Reliability in LLM-based Human Behavior Simulation
Pei Wang, Lei Wang, Yuanzi Li +1
Large language models (LLMs) are increasingly used to simulate human survey responses and behavioral reactions, yet unreliable simulations can mislead social science conclusions. H…
Analyzing and Correcting Benevolence Bias in Large Language Models
Yuanzi Li, Junhao Wang, Minghui Liu +9
Large language models (LLMs) are increasingly used as stand-ins for human respondents, from opinion polls and simulated survey participants to agent-based social simulations. These…
Benchmarking LLMs for Community Governance Simulation with Life-history Narratives
Xu Chen, Yuanzi Li, Lei Wang +6
Effective community governance hinges on understanding what specific residents think and need. Recent work has used large language models (LLMs) to simulate human respondents, offe…
Divergence Meets Consensus: A Multi-Source Negative Sampling Framework for Sequential Recommendation
Yuanzi Li, Lingjie Wang, Jingyu Zhao +4
Negative sampling is significant for training sequential recommendation models under implicit feedback. The predominant strategy, self-guided hard negative sampling, selects negati…
LLM Agents as Social Scientists: A Human-AI Collaborative Platform for Social Science Automation
Lei Wang, Yuanzi Li, Jinchao Wu +4
Traditional social science research often requires designing complex experiments across vast methodological spaces and depends on real human participants, making it labor-intensive…