2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.IR2025
Leveraging LLMs to Evaluate Usefulness of Document
Xingzhu Wang, Erhan Zhang, Yiqun Chen +7
The conventional Cranfield paradigm struggles to effectively capture user satisfaction due to its weak correlation between relevance and satisfaction, alongside the high costs of r…
cs.IR2025★ 1 cited
Exploring Human-Like Thinking in Search Simulations with Large Language Models
Erhan Zhang, Xingzhu Wang, Peiyuan Gong +2
Simulating user search behavior is a critical task in information retrieval, which can be employed for user behavior modeling, data augmentation, and system evaluation. Recent adva…
cs.IR2024★ 2 cited
USimAgent: Large Language Models for Simulating Search Users
Erhan Zhang, Xingzhu Wang, Peiyuan Gong +2
Due to the advantages in the cost-efficiency and reproducibility, user simulation has become a promising solution to the user-centric evaluation of information retrieval systems. N…