3 papers
cs.LG2026
Understanding and Mitigating Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks
Miaomiao Li, Hao Chen, Yang Wang +5
Generating synthetic datasets via large language models (LLMs) has emerged as a promising approach to improve LLM performance. However, LLMs inherently reflect biases in their trai…
cs.IR2025
Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs
Haoran Xin, Ying Sun, Chao Wang +3
Despite the success of recommender systems in alleviating information overload, fairness issues have raised concerns in recent years, potentially leading to unequal treatment for c…
cs.IR2025
LLMs as Better Recommenders with Natural Language Collaborative Signals: A Self-Assessing Retrieval Approach
Haoran Xin, Ying Sun, Chao Wang +2
Incorporating collaborative information (CI) effectively is crucial for leveraging LLMs in recommendation tasks. Existing approaches often encode CI using soft tokens or abstract i…