Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models (2025)

Abstract: Although the multilingual capability of LLMs offers new opportunities to overcome 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 cross-language RAG-based information search setting. We found that LLMs displayed systemic bias towards information in the same language as the query language in both document retrieval and answer generation. Furthermore, in scenarios where no information is in the language of the query, LLMs prefer documents in high-resource languages during generation, potentially 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 filter bubbles further marginalizing low-resource views.

## background and motivation with retrieval augmented generation (rag), large language models (llms) can leverage external knowledge and respond to questions about ongoing events @xcite . more importantly, recent strides in llm's multilingual capabilities @xcite , supporting over 100 languages, offer new opportunities to break the language-specific filter bubble and provide more equitable information access for people with diverse linguistic preferences, cultural backgrounds, and geographical locations. however, achieving information parity is challenging because of the intrinsic complexity of how information is created and represented in different linguistic contexts. this complexity stems from a ## measures in this paper, we define native language as having the same language of the query and foreign language as having a different language of the query. ## discussion through a comprehensive evaluation of current multilingual llm-based information systems, we found a clear preference for documents in the native language of queries and foreign documents in high-resource languages. given the premise of multilingual llms, breaking the linguistic barriers and filter bubbles for global users, our results highlight a significant risk in providing information parity and motivate further research. below, we discuss our main findings and their implications. ## conclusion the recent surge in multilingual large language models (llms) and retrieval augmented generation (rag) has significantly expanded conversational search across varied linguistic and cultural demographics. this technology promises to transcend linguistic barriers, offering users access to diverse perspectives. in this study, involving 13 multilingual llms, 170 documents, five languages, and seven query types, we observe that rather than facilitating a breadth of perspectives, current multilingual llms struggle with disparities across languages in the following ways: they 1) show a systematic preference for documents in the native language of the query, 2) exhibit bias favoring higherresource languages over lower-resource languages when generating responses without relevant documents in the native language of the query, 3) fail to generate responses from "both" documents and, 4) show this systematic biases across different querytypes. this failure to offer consistent information across different querying styles and languages leads to linguistic filter bubbles. these issues raise concerns about information parity, potentially leading to polarization and impeding constructive communication across communities.