13 citations · 28 across the 15 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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…
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…
Stanceosaurus 2.0: Classifying Stance Towards Russian and Spanish Misinformation
Anton Lavrouk, Ian Ligon, Tarek Naous +3
The Stanceosaurus corpus (Zheng et al., 2022) was designed to provide high-quality, annotated, 5-way stance data extracted from Twitter, suitable for analyzing cross-cultural and c…
Reducing Privacy Risks in Online Self-Disclosures with Language Models
Yao Dou, Isadora Krsek, Tarek Naous +4
Self-disclosure, while being common and rewarding in social media interaction, also poses privacy risks. In this paper, we take the initiative to protect the user-side privacy asso…
Revisiting non-English Text Simplification: A Unified Multilingual Benchmark
Michael J. Ryan, Tarek Naous, Wei Xu
Recent advancements in high-quality, large-scale English resources have pushed the frontier of English Automatic Text Simplification (ATS) research. However, less work has been don…
ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment
Tarek Naous, Michael J. Ryan, Anton Lavrouk +2
We present a comprehensive evaluation of large language models for multilingual readability assessment. Existing evaluation resources lack domain and language diversity, limiting t…
Having Beer after Prayer? Measuring Cultural Bias in Large Language Models
Tarek Naous, Michael J. Ryan, Alan Ritter +1
As the reach of large language models (LMs) expands globally, their ability to cater to diverse cultural contexts becomes crucial. Despite advancements in multilingual capabilities…
Stanceosaurus: Classifying Stance Towards Multilingual Misinformation
Jonathan Zheng, Ashutosh Baheti, Tarek Naous +2
We present Stanceosaurus, a new corpus of 28,033 tweets in English, Hindi, and Arabic annotated with stance towards 251 misinformation claims. As far as we are aware, it is the lar…
Empathetic BERT2BERT Conversational Model: Learning Arabic Language Generation with Little Data
Tarek Naous, Wissam Antoun, Reem A. Mahmoud +1
Enabling empathetic behavior in Arabic dialogue agents is an important aspect of building human-like conversational models. While Arabic Natural Language Processing has seen signif…