Language Models' Factuality Depends on the Language of Inquiry
Authors: name: Tushar Aggarwal, name: Kumar Tanmay, name: Ayush Agrawal, name: Kumar Ayush, name: Hamid Palangi, name: Paul Pu Liang

Multilingual language models (LMs) are expected to recall factual knowledge
consistently across languages, yet they often fail to transfer knowledge
between languages even when they possess the correct information in one of the
languages. For example, we find that an LM may correctly identify Rashed Al
Shashai as being from Saudi Arabia when asked in Arabic, but consistently fails
to do so when asked in English or Swahili. To systematically investigate this
limitation, we introduce a benchmark of 10,000 country-related facts across 13
languages and propose three novel metrics: Factual Recall Score, Knowledge
Transferability Score, and Cross-Lingual Factual Knowledge Transferability
Score-to quantify factual recall and knowledge transferability in LMs across
different languages. Our results reveal fundamental weaknesses in today's
state-of-the-art LMs, particularly in cross-lingual generalization where models
fail to transfer knowledge effectively across different languages, leading to
inconsistent performance sensitive to the language used. Our findings emphasize
the need for LMs to recognize language-specific factual reliability and
leverage the most trustworthy information across languages. We release our
benchmark and evaluation framework to drive future research in multilingual
knowledge transfer.