The Linguistic Connectivities Within Large Language Models (2025)

Abstract: Large language models (LLMs) have demonstrated remarkable multilingual abilities in various applications. Unfortunately, recent studies have discovered that there exist notable disparities in their performance across different languages. Understanding the underlying mechanisms behind such disparities is crucial ensuring equitable access to LLMs for a global user base. Therefore, this paper conducts a systematic investigation into the behaviors of LLMs across 27 different languages on 3 different scenarios, and reveals a Linguistic Map correlates with the richness of available resources and linguistic family relations. Specifically, high-resource languages within specific language family exhibit greater knowledge consistency and mutual information dissemination, while isolated or low-resource languages tend to remain marginalized. Our research sheds light on a deep understanding of LLM’s cross-language behavior, highlights the inherent biases in LLMs within multilingual environments and underscores the need to address these inequities.

## introduction the rapid development of large language models (llms) in recent years has marked a significant leap forward in the field of artificial intelligence @xcite . due to the massive scale of multilingual pre-training corpora, current llms have demonstrated remarkable capabilities @xcite @xcite , particularly in their ability to understand and generate text across a multitude of languages @xcite @xcite . as llms become integral to various applications, high-resource languages in the indo-european language family low-resource languages in the dravidian language family languages in the isolated sino-tibetan language family chinese information dissemination between languages figure 1 : the illustration of linguistic map within llms on several representative languages. highresource languages (e.g. english and french) within specific language family such as indo-european exhibit greater mutual information dissemination, while isolated (e.g. chinese) or low-resource languages (e.g. malayalam, kannada) tend to remain marginalized. despite the remarkable multilingual abilities displayed by llms, previous studies reveal notable disparities in their performance across different languages @xcite @xcite . for instance, highresource languages like english typically exhibit superior performance in some llms compared to low-resource languages @xcite @xcite . such inconsistencies can lead to unequal knowledge representation and biased information dissemination @xcite @xcite . with the rapid development and increasingly widespread application of llms, this imbalance not only restricts the universal applicability of these models but also exacerbates existing inequalities in language resource distribution. therefore, it is imperative to conduct indepth research on the cross-linguistic performance of llms to understand the underlying causes and implications of these discrepancies. to this end, this paper seeks to explore the consistency of knowledge expression and information dissemination in llms across different languages. specifically, we aim to address the following 2 critical research questions: to answer these questions, as illustrated in figure 2 , we design a novel cross-lingual knowledge analysis framework, and conduct a systematic investigation into the behaviors of llms across different languages. through the evaluation and analysis of the performance of 8 llms across 27 different languages, we discover that llms exhibit a linguistic map, shaped by the richness of available resources and linguistic family relationships. as illustrated in figure 1 , such linguistic map reveals how information is shared and communicated among different languages within llms. high-resource languages within specific language families exhibit greater knowledge consistency and can facilitate mutual information dissemination. in contrast, languages belonging to isolated language families or those with limited resources tend to occupy a more isolated position. specifically, we first investigate the consistency of cross-linguistic knowledge expression in different models. for a given knowledge-based question, we pose queries in 27 different languages to a llm and measure the consistency among the answers provided for each language. our experiments reveal a widespread phenomenon of crosslinguistic inconsistency in knowledge expression. to further analyze the underlying patterns, we use elinguistic foot_0 to calculate the distance between languages based on genetic proximity and find that there is a significant correlation between linguistic proximity and the consistency of cross-linguistic knowledge expression within llms. this finding underscores that linguistic proximity plays a crucial role in the uniformity of llm performance. furthermore, to gain a deeper understanding of how linguistic differences affect the propagation of information in llms, we explore two critical scenarios for llm communication: misinformation dissemination and multi-hop knowledge editing. for misinformation dissemination, we introduce documents into the context of specific language queries. these documents are consistent in content but varied in language, and we then compare their impact on the llm's responses. for multi-hop knowledge editing, we provide the edited knowledge of different languages into the context of specific language queries, and observe whether llms can apply the edited knowledge for knowledge reasoning. we find that these 8 llms demonstrate consistent phenomena in 2 scenarios: introducing information in high-resource languages from the same language family had a greater influence on the llm's outputs, leading to information propagation within these language groups. in contrast, languages from independent (e.g., chinese) and low-resource families were minimally influenced by, and had little effect on, other languages, highlighting significant interlinguistic barriers. in summary, our study reveals that due to factors such as differences in resource richness and genetic proximity, significant communication barriers exist between languages within llms, leading to the formation of a linguistic map within llms. this study highlights the inherent biases present in llms within multilingual environments and help researchers comprehend the underlying mechanisms driving cross-linguistic disparities in llms. additionally, our findings highlight the importance of addressing these inconsistencies to promote fairness and inclusivity in multilingual ai applications. consequently, this work inspires future research to focus on bridging language gaps, ensuring equitable performance across all languages, and enhancing the robustness of llms in diverse linguistic contexts. in this section, we investigate the consistency of knowledge expression across different languages. specially, by testing the model's responses to queries in different languages with identical content, we observe that the phenomenon of linguistic inconsistency is widespread in llms. furthermore, through an analysis of the correlation between consistency across language pairs and the linguistic distance between them, we find that languages with closer distances tend to show higher consistency. in the following, we will first introduce the experimental setups and then provide a detailed explanation of these findings. ## cross-lingual information dissemination conclusion 2. the pattern of information dissemination is structured according to language family relationships and resource availability. high-resource languages within specific language families facilitate the mutual dissemination of information. in this section, to explore the patterns of information propagation across languages within llms, we developed an analysis framework that includes two key scenarios: cross-linguistic misinformation propagation and multi-hop crosslingual knowledge editing. our experiments reveal that different llms exhibit similar information exchange patterns in these scenarios. specifically, high-resource languages within specific families tend to exchange information more easily with each other, while languages from independent families or low-resource languages remain relatively isolated. we define this phenomenon as the linguistic map within llms. this finding highlights the inherent bias in llms within multilingual environment and underscores the importance of improving fairness across languages. in the following sections, we will first introduce the experimental setups, then explain how the corresponding findings are obtained.