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cs.CL2025
MAO-ARAG: Multi-Agent Orchestration for Adaptive Retrieval-Augmented Generation
Yiqun Chen, Erhan Zhang, Lingyong Yan +4
In question-answering (QA) systems, Retrieval-Augmented Generation (RAG) has become pivotal in enhancing response accuracy and reducing hallucination issues. The architecture of RA…
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…