7 papers
Camellia: Benchmarking Cultural Biases in LLMs for Asian Languages
Tarek Naous, Anagha Savit, Carlos Rafael Catalan +17
As Large Language Models (LLMs) develop stronger multilingual capabilities, their sensitivity to culturally diverse entities becomes increasingly important. Prior work by Naous et…
To Lie or Not to Lie? Investigating The Biased Spread of Global Lies by LLMs
Zohaib Khan, Mustafa Dogan, Ifeoma Okoh +6
Misinformation is on the rise, and the strong writing capabilities of LLMs lower the barrier for malicious actors to produce and disseminate false information. We study how LLMs be…
Flipping the Dialogue: Training and Evaluating User Language Models
Tarek Naous, Philippe Laban, Wei Xu +1
Conversations with LMs involve two participants: a human user leading the conversation, and an LM assistant responding to the user's request. To satisfy this specific role, LMs are…
What are Foundation Models Cooking in the Post-Soviet World?
Anton Lavrouk, Tarek Naous, Alan Ritter +1
The culture of the Post-Soviet states is complex, shaped by a turbulent history that continues to influence current events. In this study, we investigate the Post-Soviet cultural f…
CARE: Multilingual Human Preference Learning for Cultural Awareness
Geyang Guo, Tarek Naous, Hiromi Wakaki +4
Language Models (LMs) are typically tuned with human preferences to produce helpful responses, but the impact of preference tuning on the ability to handle culturally diverse queri…
On The Origin of Cultural Biases in Language Models: From Pre-training Data to Linguistic Phenomena
Tarek Naous, Wei Xu
Language Models (LMs) have been shown to exhibit a strong preference towards entities associated with Western culture when operating in non-Western languages. In this paper, we aim…