Same-Language Country Pairs

• Same-language country pairs are nation pairings where a shared language exhibits distinct regional, sociolinguistic, and digital traits.
• Research employs large-scale digital corpora, quantitative mapping, and regional models to assess lexical, phonological, and cultural divergences.
• These studies inform improved multilingual models, bias mitigation, and targeted resource allocation for diverse linguistic contexts.

Same-language country pairs are nation pairings in which a single language is spoken by substantial populations across both countries, yet the language manifests distinct regional, sociolinguistic, and digital characteristics in each locale. This concept bridges linguistic typology, computational dialectology, corpus construction, multilingual modeling, and cultural representation. In contemporary language technologies and empirical research, identifying and rigorously modeling same-language country pairs enables fine-grained analysis of national varieties, evaluation of biases in computational models, and deeper understanding of cross-country cultural and communicative variation.

1. Methodologies for Mapping and Identifying Same-Language Country Pairs

Quantitative language mapping frameworks leverage large-scale digital corpora for granular analysis of language–country associations. In "Mapping Languages: The Corpus of Global Language Use" (Dunn, 2020), web-crawled data is partitioned by geo-referenced domains to produce country–language sub-corpora (e.g., English–Canada, English–UK). The idNet model is deployed for robust language identification, utilizing character trigram frequencies projected into a 216,000-dimensional hashing space and a three-layer MLP architecture:

$$y = \mathrm{softmax}(W_3 f(W_2 f(W_1 x + b_1) + b_2) + b_3)$$

Here, $x$ encodes input trigram frequencies and $y$ is the probability distribution over 464 languages.

Regional models for language identification further refine this approach by restricting candidate languages according to expected national distributions, thus increasing accuracy, particularly for low-resource and regionally-concentrated languages (Dunn et al., 14 Mar 2024). Under this paradigm, same-language country pairs are recognized and modeled as distinct entities, each tied to linguistic practices unique to their national context.

2. Corpus Construction and Comparative Metrics

Comprehensive corpora are indispensable for empirically characterizing same-language country pairs. The CGLU corpus (Dunn, 2020) comprises 423 billion words, 148 languages, and 158 countries, yielding almost 2,000 distinct language–country sub-corpora, thereby facilitating direct quantitative comparison across national varieties of a single language.

Comparison of linguistic output between those sub-corpora is operationalized using frequency-based similarity measures; Spearman correlations of unigram frequency rankings reveal the proximity of lexical usage across national varieties. For “inner-circle” English varieties (US, UK, Canada, Australia), typical corpus similarity scores approach 0.70, indicating high but not total alignment. For other languages, scores are markedly lower—e.g., Arabic or Portuguese varieties across different countries—suggesting more pronounced country-specific divergence in language use. Cross-platform comparisons (web vs. Twitter corpora) highlight additional domain and register effects, with substantial variation in digital language production practices between countries.

3. LLMs, Geographic Priors, and Regional Biases

Multilingual LLMs and PLMs encode complex patterns of country associations. The Geographic-Representation Probing Framework (Faisal et al., 2022) leverages self-conditioning and expert unit extraction to probe country–concept representations within PLMs. These are compared using metrics such as Neighborhood Score:

$$N_s(G) = \sum_{c_i \in G} (|N_i| + \sum_{j \in N_i} |N_j|)$$

Neighborhood scores and clustering reveal that models internalize physical/geographic proximity in their representations. However, these associations are unevenly distributed across languages within models and routinely subject to geopolitical favoritism—the disproportionate referencing of globally dominant countries (e.g., USA, Western Europe) in generated outputs.

Region-specific language identification models improve upon traditional “global” models by considering only the locally-expected languages plus a standard set of 31 international lingua franca, yielding improved f-scores (by up to 10.4 percentage points in some regions) (Dunn et al., 14 Mar 2024). This leads to more accurate digital language maps, especially for low-resource languages and nuanced national varieties within same-language country pairs.

4. Country Similarity and Cross-lingual Transfer

Country similarity—the degree to which languages are deployed across overlapping sets of countries—is a key predictor of multilingual model performance (Nezhad et al., 17 Dec 2024). Its quantitative expression is the Jaccard similarity:

$J(A, B) = \frac{|C_A \cap C_B|}{|C_A \cup C_B|}$

where $C_A$ and $C_B$ are the sets of countries where languages $A$ and $B$ are spoken. Country similarity measures, reduced via Multi-Dimensional Scaling (MDS), capture sociopolitical, cultural, and institutional commonalities highly relevant to language modeling.

Regression analyses employing SHAP values confirm country similarity as a pivotal feature for transfer learning and cross-lingual representation. For same-language country pairs, this implies that shared cultural and lexical patterns across nations potentiate more robust and equitable LLMs, accelerating resource transfer and reducing performance disparities for underrepresented linguistic contexts.

5. Mutual Intelligibility and Dialectal Modeling

Mutual intelligibility quantifies the degree to which speakers of different national varieties of the same language can comprehend one another. The Linear Discriminative Learner (LDL) (Nieder et al., 5 Feb 2024) operationalizes this by mapping phonological cues to semantic vectors using matrices $C$ (phonological), $S$ (semantic), and $F$ (weights):

$C \cdot F = S$

The inclusion of multilingual sound classes and embeddings (e.g., ConceptNet Numberbatch) enables assessment of semantic overlap and phonological similarity across national varieties.

Experiments show that comprehension accuracy depends critically on chunk size and morphological trimming, with phonological abstraction and semantic alignment yielding results congruent with human psycholinguistic data. For same-language country pairs with divergent dialects or regional morphologies, automated methods such as LDL provide objective and reproducible metrics for mutual intelligibility assessment, bridging cognitive modeling and computational testing.

6. Cultural Representation and Model Internal Dynamics

Recent work integrates same-language country pairs into probing the cultural understanding of LLMs (Cho et al., 18 Oct 2025). By tracing activation path overlaps when answering semantically equivalent questions (varying only the target country but keeping language constant), researchers observe that most same-language pairs activate highly similar circuits. Weighted Jaccard similarity across path attributions quantifies these overlaps:

$$\mathrm{Sim}(P(Q_{L,C_1}), P(Q_{L,C_2})) = \frac{\sum_e \min(w_e^{(1)}, w_e^{(2)})}{\sum_e \max(w_e^{(1)}, w_e^{(2)})}$$

High overlap values for US–UK and Spain–Mexico pairs confirm that language form governs much of the internal computation, entangling cultural traits with linguistic cues. Notably, anomalous cases such as South Korea–North Korea exhibit low and variable overlaps, indicating that cultural or political sensitivities can override linguistic similarity in internal model representations.

This suggests that LLMs store, retrieve, and process cultural knowledge in a dominantly language-specific manner, often failing to disentangle language from culture except in marked cases of geopolitical divergence.

7. Research Applications and Future Directions

The explicit modeling and empirical analysis of same-language country pairs underpin advancements in computational dialectology, sociolinguistics, digital demography, and NLP fairness. Applications include:

• Construction of regionally nuanced corpora for benchmarking, fine-tuning, and evaluation.
• Targeted resource augmentation, informed by country similarity metrics, to improve underrepresented language coverage.
• Systematic assessment of dialectal, lexical, and stylistic divergence using statistical and cognitive modeling.
• Bias mitigation in multilingual LLMs—addressing over-amplification of dominant cultures and improving regional representativeness.
• Enhanced policy-making and educational resource allocation based on granular mappings of language–country distributions.

Given the evidence that LLMs encode both linguistic and extralinguistic (cultural, geopolitical) factors in complex ways, further research should focus on disentangling these dimensions and refining model architectures to ensure equitable and context-sensitive representation of same-language country pairs.