BASES: Large-scale Web Search User Simulation with Large Language Model
  based Agents
Ruiyang Ren Peng Qiu Yingqi Qu Jing Liu Wayne Xin Zhao Hua Wu Ji-Rong Wen Haifeng Wang
Due to the excellent capacities of large language models (LLMs), it becomes
feasible to develop LLM-based agents for reliable user simulation. Considering
the scarcity and limit (e.g., privacy issues) of real user data, in this paper,
we conduct large-scale user simulation for web search, to improve the analysis
and modeling of user search behavior. Specially, we propose BASES, a novel user
simulation framework with LLM-based agents, designed to facilitate
comprehensive simulations of web search user behaviors. Our simulation
framework can generate unique user profiles at scale, which subsequently leads
to diverse search behaviors. To demonstrate the effectiveness of BASES, we
conduct evaluation experiments based on two human benchmarks in both Chinese
and English, demonstrating that BASES can effectively simulate large-scale
human-like search behaviors. To further accommodate the research on web search,
we develop WARRIORS, a new large-scale dataset encompassing web search user
behaviors, including both Chinese and English versions, which can greatly
bolster research in the field of information retrieval. Our code and data will
be publicly released soon.