Abstract Large Language Models (LLMs) are prone to factuality hallucination, generating
text that contradicts established knowledge. While extensive research has addressed this in …

The success of Large Language Models (LLMs) in multicultural environments hinges on
their ability to understand users' diverse cultural backgrounds. We measure this capability by …

Large-scale deployment of large language models (LLMs) in various applications, such as
chatbots and virtual assistants, requires LLMs to be culturally sensitive to the user to ensure …

The dissemination of false information on online platforms presents a serious societal
challenge. While manual fact-checking remains crucial, Large Language Models (LLMs) …

While a large body of work inspects language models for biases concerning gender, race,
occupation and religion, biases of geographical nature are relatively less explored. Some …

Large language models (LLMs) encode vast amounts of world knowledge acquired via
training on large web-scale datasets crawled from the internet. However, these datasets …

Recent research demonstrates geographic biases in various Large Language Models that
reflects common human biases, which are presumably present in the training data. We …

Evaluating the factuality of long-form large language model (LLM)-generated text is an
important challenge. Recently there has been a surge of interest in factuality evaluation for …

In recent years, deep learning and computer vision have driven significant advancements in
visual recognition tasks. However, these models often overlook a critical aspect: fairness …

The widespread adoption of machine learning models into digital platforms, spanning
general-purpose applications such as chatbots, professional tools like code generation, and …