  With Retrieval Augmented Generation (RAG), Large Language Models (LLMs) are
playing a pivotal role in information search and are being adopted globally.
Although the multilingual capability of LLMs offers new opportunities to bridge
the language barrier, do these capabilities translate into real-life scenarios
where linguistic divide and knowledge conflicts between multilingual sources
are known occurrences? In this paper, we studied LLM's linguistic preference in
a RAG-based information search setting. We found that LLMs displayed systemic
bias towards information in the same language as the query language in both
information retrieval and answer generation. Furthermore, in scenarios where
there is little information in the language of the query, LLMs prefer documents
in high-resource languages, reinforcing the dominant views. Such bias exists
for both factual and opinion-based queries. Our results highlight the
linguistic divide within multilingual LLMs in information search systems. The
seemingly beneficial multilingual capability of LLMs may backfire on
information parity by reinforcing language-specific information cocoons or
filter bubbles further marginalizing low-resource views.
