Language Specific Knowledge: Do Models Know Better in X than in English?
Authors: name: Ishika Agarwal, name: Nimet Beyza Bozdag, name: Dilek Hakkani-Tür

Code-switching is a common phenomenon of alternating between different
languages in the same utterance, thought, or conversation. We posit that humans
code-switch because they feel more comfortable talking about certain topics and
domains in one language than another. With the rise of knowledge-intensive
language models, we ask ourselves the next, natural question: Could models hold
more knowledge on some topics in some language X? More importantly, could we
improve reasoning by changing the language that reasoning is performed in? We
coin the term Language Specific Knowledge (LSK) to represent this phenomenon.
As ethnic cultures tend to develop alongside different languages, we employ
culture-specific datasets (that contain knowledge about cultural and social
behavioral norms). We find that language models can perform better when using
chain-of-thought reasoning in some languages other than English, sometimes even
better in low-resource languages. Paired with previous works showing that
semantic similarity does not equate to representational similarity, we
hypothesize that culturally specific texts occur more abundantly in
corresponding languages, enabling specific knowledge to occur only in specific
"expert" languages. Motivated by our initial results, we design a simple
methodology called LSKExtractor to benchmark the language-specific knowledge
present in a language model and, then, exploit it during inference. We show our
results on various models and datasets, showing an average relative improvement
of 10% in accuracy. Our research contributes to the open-source development of
language models that are inclusive and more aligned with the cultural and
linguistic contexts in which they are deployed.