3 papers
cs.CL2026
SafeCRS: Personalized Safety Alignment for LLM-Based Conversational Recommender Systems
Haochang Hao, Yifan Xu, Xinzhuo Li +2
Current LLM-based conversational recommender systems (CRS) primarily optimize recommendation accuracy and user satisfaction. We identify an underexplored vulnerability in which rec…
cs.CL2026
Context-Aware Counterfactual Data Augmentation for Gender Bias Mitigation in Language Models
Shweta Parihar, Liu Guangliang, Natalie Parde +1
A challenge in mitigating social bias in fine-tuned language models (LMs) is the potential reduction in language modeling capability, which can harm downstream performance. Counter…
cs.CL2026
Evaluating Social Bias in RAG Systems: When External Context Helps and Reasoning Hurts
Shweta Parihar, Lu Cheng
Social biases inherent in large language models (LLMs) raise significant fairness concerns. Retrieval-Augmented Generation (RAG) architectures, which retrieve external knowledge so…