2 papers
cs.CL2025
Enhancing the Preference Extractor in Multi-turn Dialogues: From Annotating Disasters to Accurate Preference Extraction
Cheng Wang, ziru Liu, Pengcheng Tang +3
Identifying user preferences in dialogue systems is a pivotal aspect of providing satisfying services. Current research shows that using large language models (LLMs) to fine-tune a…
cs.HC2025
RecUserSim: A Realistic and Diverse User Simulator for Evaluating Conversational Recommender Systems
Luyu Chen, Quanyu Dai, Zeyu Zhang +6
Conversational recommender systems (CRS) enhance user experience through multi-turn interactions, yet evaluating CRS remains challenging. User simulators can provide comprehensive…