6 papers
Evaluating Reliability Asymmetries in Chinese Factual Search and AI Answers
Geng Liu, Li Feng, Mengxiao Zhu +1
Search engines and AI-powered systems increasingly mediate access to factual information, yet their reliability remains difficult to evaluate in realistic information-seeking setti…
Probing Social Identity Bias in Chinese LLMs with Gendered Pronouns and Social Groups
Geng Liu, Feng Li, Junjie Mu +2
Large language models (LLMs) are increasingly deployed in user-facing applications, raising concerns that they may reflect and amplify social biases. We investigate social identity…
Evaluating Chinese Large Language Models: The Influence of Persona Assignment on Stereotypes and Safeguards
Geng Liu, Li Feng, Carlo Alberto Bono +3
Recent research has highlighted that assigning specific personas to large language models (LLMs) can significantly increase harmful content generation. However, limited attention h…
Towards an Automated Framework to Audit Youth Safety on TikTok
Linda Xue, Francesco Corso, Nicolo' Fontana +3
This paper investigates the effectiveness of TikTok's enforcement mechanisms for limiting the exposure of harmful content to youth accounts. We collect over 7000 videos, classify t…
Analyzing the Safety of Japanese Large Language Models in Stereotype-Triggering Prompts
Akito Nakanishi, Yukie Sano, Geng Liu +1
In recent years, Large Language Models have attracted growing interest for their significant potential, though concerns have rapidly emerged regarding unsafe behaviors stemming fro…
Comparing diversity, negativity, and stereotypes in Chinese-language AI technologies: an investigation of Baidu, Ernie and Qwen
Geng Liu, Carlo Alberto Bono, Francesco Pierri
Large Language Models (LLMs) and search engines have the potential to perpetuate biases and stereotypes by amplifying existing prejudices in their training data and algorithmic pro…