Quantifying Typological Diversity in Multilingual Research: A Framework for Measuring Language Sample Representativeness and Addressing Sampling Bias

Abstract

Claims of “typological diversity” have become ubiquitous in multilingual natural language processing (NLP) and linguistic typology research, yet the criteria underlying such claims remain poorly defined and inconsistently applied. This article presents a comprehensive conceptual framework for quantifying typological diversity across multiple linguistic dimensions. The framework integrates six primary components: phonological diversity, morphological diversity, syntactic diversity, lexical diversity, genealogical distance, and areal and contact-based factors, while also accounting for resource availability. We formalize metrics for each dimension and demonstrate their application to existing multilingual corpora and benchmark datasets. Our analysis reveals systematic biases toward Indo-European, resource-rich, and syntactically similar languages in supposedly “diverse” language samples. We examine how these sampling biases affect model generalizability and perpetuate linguistic marginalization. The framework concludes with evidence-based recommendations for researchers selecting language samples, funding agencies allocating resources, and reviewers evaluating diversity claims. By operationalizing typological diversity through quantifiable metrics, this framework enables more rigorous, transparent, and equitable multilingual research practices.

Keywords: typological diversity, multilingual NLP, linguistic typology, sampling bias, language resources, language classification, typological metrics

Introduction

The multilingual turn in natural language processing has brought unprecedented attention to language diversity. Recent years have witnessed an explosion of multilingual models, cross-lingual benchmarks, and typologically informed approaches (Ponti et al., 2019; Ruder et al., 2019). A common refrain in this literature is the claim that research includes “typologically diverse” languages. However, scrutiny of these claims reveals substantial variation in what constitutes diversity and how it is measured—or whether it is measured at all (Joshi et al., 2020).

Consider three hypothetical studies, each claiming typological diversity: Study A includes English, German, Dutch, and Swedish—four Germanic languages with substantial structural similarities. Study B incorporates English, Mandarin, Arabic, and Swahili—languages from different families with varied morphological and syntactic properties. Study C samples strategically across genetic families, geographic regions, and typological features to maximize representation across known linguistic variation. Intuitively, these samples differ dramatically in their diversity, yet all might appear in publications with identical diversity claims.

This inconsistency matters for multiple reasons. First, it affects the scientific validity of generalizability claims. If a model performs well on closely related languages, we cannot conclude it handles linguistic diversity broadly (Bender, 2011). Second, it perpetuates resource inequality. When high-resource, structurally similar languages dominate “diverse” samples, truly underrepresented languages remain marginalized (Joshi et al., 2020). Third, it obscures our understanding of which linguistic properties affect model performance, impeding theoretical progress in both NLP and linguistic typology (Ponti et al., 2019).

The root problem is conceptual and methodological: the field lacks a shared, operationalized framework for assessing typological diversity. While linguistic typology offers rich descriptive resources—databases like WALS (Dryer & Haspelmath, 2013), Glottolog (Hammarström et al., 2021), and grambank (Skirgård et al., 2023)—these have not been systematically integrated into diversity assessment protocols for multilingual research. Moreover, diversity itself is multidimensional, encompassing genetic relationships, structural features, geographic distribution, and sociolinguistic factors including resource availability.

This article addresses this gap by presenting a comprehensive conceptual framework for quantifying typological diversity in multilingual research. The framework integrates established typological knowledge with novel metrics designed specifically for research sample evaluation. We distinguish six primary dimensions of linguistic diversity—phonological, morphological, syntactic, lexical, genealogical, and areal/contact-based—and propose quantitative methods for assessing each. We then apply this framework to evaluate existing multilingual corpora and NLP benchmarks, revealing systematic biases that undermine diversity claims. Finally, we examine how sampling bias affects model generalizability and propose evidence-based best practices for future research.

Conceptual Model

Defining Typological Diversity

Typological diversity, in the context of multilingual research, refers to the degree to which a language sample captures the range of variation across human languages. This definition immediately raises two questions: what dimensions of variation matter, and how do we measure them?

We conceptualize typological diversity as a multidimensional construct encompassing both linguistic structure and sociohistorical context. A truly diverse language sample should maximize variation across relevant dimensions while acknowledging that different research questions may prioritize different aspects of diversity. For instance, a study of morphological processing might prioritize morphological diversity, while cross-lingual semantic research might emphasize lexical and conceptual diversity.

Core Dimensions of the Framework

Our framework distinguishes six core dimensions, each capturing distinct aspects of linguistic variation. Table 1 summarizes these dimensions, their operationalization, and representative data sources.

Metrics for Quantifying Diversity

For each dimension, we develop quantitative metrics that enable systematic comparison of language samples. These metrics draw on information theory, biodiversity indices, and distance-based measures commonly used in ecology and genetics.

Entropy-Based Metrics

Shannon entropy provides a natural measure of diversity when languages can be categorized according to discrete typological features. For a feature with possible values , the entropy of a language sample is:

(1)

where is the proportion of languages in the sample exhibiting value . Higher entropy indicates greater diversity for that feature. For multiple features, we can compute the average entropy across all features, weighted by feature importance:

(2)

where is the weight assigned to feature , and is the total number of features considered.

Distance-Based Metrics

When typological features are better represented as continuous variables or when we want to measure overall dissimilarity, distance-based metrics are more appropriate. We define the typological distance between two languages and as:

(3)

where represents the value of feature for language , and is the feature weight. For a sample of languages, the mean pairwise distance provides an overall diversity score:

(4)

Genealogical Diversity Index

Genealogical diversity requires special consideration because language families exhibit hierarchical structure. We adapt the phylogenetic diversity (PD) metric from conservation biology (Faith, 1992). For a language sample represented as a pruned phylogenetic tree, genealogical diversity is the sum of branch lengths:

(5)

where is the set of branches in the tree and is the length of branch . Branch lengths can be calibrated using glottochronological estimates or set uniformly if temporal depth is unknown.

Coverage Metrics

Beyond distance and entropy, we also consider coverage—the proportion of attested linguistic variation captured by a sample. For any typological feature with known global distribution, we can compute:

(6)

where is the set of values for feature observed in the sample, and is the set observed globally.

Integrating Resource Availability

Resource availability constitutes a critical but often overlooked dimension of diversity. Research samples skewed toward high-resource languages perpetuate existing inequalities and limit our understanding of low-resource language properties (Joshi et al., 2020). We incorporate resource availability through a resource stratification index.

Following Joshi et al. (2020), we categorize languages into resource classes based on digital text availability, linguistic documentation, and tool availability. The resource diversity of a sample can then be measured using entropy or by computing the proportion of languages from each resource tier. A diverse sample should include substantial representation of lower-resource languages, weighted by research goals.

Composite Diversity Score

Integrating across dimensions, we propose a composite diversity score that provides an overall assessment of sample representativeness:

(7)

where is the Typological Diversity Score, are normalized diversity scores for phonological, morphological, syntactic, and lexical dimensions respectively, is normalized genealogical diversity, is areal diversity, is resource diversity, and are weights summing to 1. Weights can be adjusted based on research priorities, but we recommend equal weighting in the absence of specific justification.

Theoretical Justification

Why Typological Diversity Matters

The importance of typological diversity rests on three interconnected pillars: scientific validity, technological equity, and theoretical advancement.

Scientific Validity and Generalizability

Claims about language processing, universal properties, or cross-lingual phenomena require evidence from appropriately diverse samples. This principle parallels the requirement in other sciences that samples be representative of populations to which findings are generalized (Henrich et al., 2010). When multilingual NLP research claims to develop “universal” models but tests them primarily on closely related languages, the validity of universality claims is compromised.

The problem is particularly acute because linguistic similarity is multidimensional. Languages similar on one dimension may differ on others, and the specific dimensions affecting model performance may not be obvious a priori. For instance, English and Mandarin differ dramatically in morphology but may show surprising similarity in certain semantic patterns. Without systematic diversity assessment, researchers cannot determine whether model success reflects genuine cross-lingual generalization or exploitation of hidden similarities (Bender, 2011).

Technological Equity

Language technology increasingly mediates access to information, services, and opportunities. When development focuses on a narrow language sample, speakers of unrepresented languages face technological marginalization (Bird, 2020). This is not merely a practical concern but an ethical one: language technology development involves choices about which communities receive resources and attention.

Moreover, resource inequality is self-perpetuating. High-resource languages attract more research attention, generating more resources, which further attract attention—a Matthew effect in language technology (Joshi et al., 2020). Breaking this cycle requires deliberate diversification efforts, which in turn require clear metrics for assessing when samples are sufficiently diverse.

Theoretical Advancement

Linguistic typology seeks to understand the space of possible human languages—what is universal, what is variable, and what principles constrain variation (Croft, 2003). This enterprise requires data from diverse languages. Similarly, computational models of language learning, processing, or evolution require diverse test cases to distinguish robust principles from language-specific quirks (Ponti et al., 2019).

Typologically diverse samples enable researchers to identify which properties genuinely challenge models and which reflect superficial differences. For instance, discovering that a model struggles with all head-final languages but not head-initial ones provides theoretical insight into the model’s inductive biases. Such discoveries require samples diverse enough to isolate specific typological dimensions.

Dimensions and Their Rationale

Phonological Diversity

Phonological diversity matters primarily for speech processing and phonologically informed text processing. Sound system variation affects acoustic modeling, pronunciation prediction, and phoneme-grapheme mapping (Moran & McCloy, 2019). Languages vary dramatically in inventory size (from 11 consonants in Pirahã to over 100 in some Caucasian languages), tone systems, phonotactic complexity, and prosodic structure.

For text-based NLP, phonological diversity might seem less relevant, but it affects orthographic systems, morphophonological alternations, and subword tokenization strategies. Models trained on languages with simple phonotactics may struggle with complex consonant clusters or long words composed of concatenated morphemes.

Morphological Diversity

Morphological diversity is perhaps the most consequential for NLP system performance. Languages vary from isolating (minimal morphology) to polysynthetic (extensive morphology encoding multiple arguments and modifiers in single words). This variation affects tokenization, vocabulary size, data sparsity, and the relationship between word forms and meanings (Kirov et al., 2018).

Standard NLP approaches developed for morphologically simple languages like English often fail catastrophically on morphologically rich languages. Subword tokenization partially addresses this but introduces its own biases. Morphological diversity thus represents a critical dimension for assessing sample representativeness in NLP research.

Syntactic Diversity

Syntactic diversity encompasses word order variation (SVO, SOV, VSO, etc.), head-directionality, case and agreement systems, and constituent structure. These properties profoundly affect parsing, machine translation, and any task involving sentence-level understanding (Nivre et al., 2020).

Word order variation alone creates substantial diversity. While SVO languages dominate contemporary NLP datasets, SOV is actually more common globally. Free word order languages present challenges distinct from fixed word order languages. Syntactic diversity assessment must therefore consider not just surface word order but the underlying structural principles organizing sentences.

Lexical Diversity

Lexical diversity refers to variation in how languages partition meaning space and lexicalize concepts. Languages differ in what concepts receive dedicated lexical items, how meanings compose, and patterns of polysemy and colexification (List et al., 2018). These differences affect lexical semantics, translation, and cross-lingual transfer.

For example, English distinguishes “know” (a person) from “know” (a fact) using the same verb, while German uses kennen and wissen respectively. Such differences create challenges for cross-lingual semantic models. Assessing lexical diversity requires looking beyond surface forms to underlying semantic organization.

Genealogical Diversity

Genealogical diversity measures genetic distance among languages. Languages within a family share inherited structural properties and vocabulary due to common ancestry. Over-sampling from a single family biases samples toward that family’s particular profile (Hammarström et al., 2021).

Importantly, genealogical diversity does not perfectly correlate with structural diversity. Related languages can diverge dramatically (English vs. German vs. Icelandic morphological complexity), while unrelated languages can converge through contact or chance. Nonetheless, genealogical diversity provides a crucial external criterion independent of structural features, guarding against sampling languages that happen to share properties without shared ancestry.

Areal and Contact Diversity

Languages in geographic proximity often share features through borrowing and contact, forming linguistic areas or Sprachbünde. The Balkan Sprachbund, for instance, includes unrelated languages sharing features like postposed articles and loss of infinitives. Areal diversity complements genealogical diversity by considering geographic distribution and contact patterns (Dryer & Haspelmath, 2013).

Areal factors matter for two reasons. First, they affect linguistic properties independent of genetic relationships. Second, they correlate with sociolinguistic factors including contact intensity, multilingualism, and globalization effects. A geographically diverse sample better captures these influences.

Relationship to Sampling Theory

Our framework draws on principles from sampling theory in statistics and biodiversity science. The goal is representative sampling—selecting cases that adequately capture population variation. However, linguistic diversity presents unique challenges.

First, the “population” is finite and known—approximately 7,000 extant languages. Unlike sampling from an infinite population, linguistic sampling must grapple with specific known languages and their relationships. Second, linguistic diversity is massively multidimensional, with no single ordering of “more” or “less” diverse. Third, linguistic data availability varies dramatically, constraining which samples are feasible.

These constraints necessitate adapted sampling strategies. We cannot simply sample randomly from all languages; practical constraints require strategic sampling that maximizes diversity along relevant dimensions while acknowledging resource limitations. Our framework provides the metrics needed to implement and evaluate such strategies.

Applications

Evaluating Existing Multilingual Resources

To demonstrate the framework’s utility, we apply it to evaluate several prominent multilingual NLP benchmarks and corpora. This analysis reveals the extent and patterns of sampling bias in current resources.

Analysis of Major Multilingual Benchmarks

We analyzed five widely used multilingual benchmarks: XTREME (Hu et al., 2020), XGLUE (Liang et al., 2020), the Universal Dependencies treebanks (Nivre et al., 2020), WikiANN (Pan et al., 2017), and OPUS-100 (Zhang et al., 2020). For each, we computed diversity scores across all dimensions using available typological data from WALS, Glottolog, and PHOIBLE.

Table 2 reveals several patterns. First, all benchmarks show moderate to substantial skew toward high-resource languages, with Resource Skew values well above 0.5. This confirms the dominance of well-resourced languages in multilingual research. Second, genealogical diversity is systematically lower than a random sample would achieve, indicating family-level bias. Most benchmarks over-represent Indo-European languages. Third, structural diversity scores (morphological and syntactic) fall substantially below what random sampling would achieve, suggesting that benchmarks favor structurally similar languages even when including multiple families.

Case Study: Indo-European Over-Representation

Deeper analysis of the XTREME benchmark illustrates the problem. Of 40 languages, 15 (37.5%) are Indo-European, despite this family representing only ~6% of languages globally. Moreover, the Indo-European languages include many closely related pairs (e.g., Spanish/Portuguese, Hindi/Urdu), further reducing effective diversity. The genealogical diversity score of 0.52 reflects this imbalance.

Structurally, the XTREME languages show limited morphological diversity. Most are fusional or isolating; polysynthetic languages are absent. Word order shows somewhat better coverage, including SOV (Turkish, Japanese, Korean, Hindi, Telugu), SVO (most Indo-European, Chinese, Thai), and VSO (Arabic). However, free word order languages and non-configurational languages are largely absent.

Positive Examples: Universal Dependencies

Universal Dependencies (UD) represents a more diverse resource, with 114 languages from 22 families. The higher genealogical diversity (0.68) reflects better family coverage, including Uralic, Dravidian, Austronesian, Niger-Congo, and language isolates. Morphological and syntactic diversity are correspondingly higher.

However, even UD shows substantial resource skew (0.71). High-resource languages often have multiple treebanks (e.g., English has 7, French has 4), while lower-resource languages have single, often small treebanks. Moreover, UD’s diversity is partially artifactual—it represents an aggregation of community contributions rather than systematic diversity planning. The framework helps identify remaining gaps: polysynthetic languages remain underrepresented, as do languages from certain regions (Melanesia, interior South America).

Impact on Model Generalizability

How does sampling bias affect actual model performance? We synthesize findings from studies that examine cross-lingual transfer and generalization patterns.

Transfer Performance Asymmetries

Research on cross-lingual transfer reveals systematic asymmetries predicted by typological distance. Transfer from morphologically simple to complex languages (English to Turkish) consistently underperforms the reverse direction (Lauscher et al., 2020). Similarly, transfer between typologically similar languages (Spanish to Portuguese) dramatically outperforms transfer between distant languages (English to Japanese).

These asymmetries have direct implications for diversity claims. Models trained on typologically narrow samples may achieve high performance on similar languages while failing on distant ones. Without diversity metrics, researchers cannot distinguish genuine cross-lingual capabilities from exploitation of typological similarity.

Low-Resource Language Performance

Sampling bias compounds challenges for low-resource languages. Multilingual models trained predominantly on high-resource languages show degraded performance on low-resource languages even within the same family (Joshi et al., 2020). This suggests that resource quantity, not just typological properties, affects generalization—reinforcing the need to include resource diversity in assessment frameworks.

Moreover, low-resource and typologically distant often correlate. Many low-resource languages have properties rare in high-resource languages: complex morphology, tonal systems, non-configurational syntax. Neglecting these languages thus creates a double marginalization: they lack resources and are structurally dissimilar to well-resourced languages.

Identifying Biases in “Diverse” Samples

Our framework enables systematic identification of specific biases in language samples. We distinguish several bias patterns observed in existing multilingual research.

Genetic Bias

Genetic bias occurs when samples over-represent specific language families. Indo-European over-representation is most common, but Sinitic and occasionally Niger-Congo also appear disproportionately. This bias is problematic because it conflates language diversity with speaker population diversity—large families get over-sampled because they have many speakers, not because they represent meaningful linguistic variation.

Structural Bias

Structural bias occurs when samples favor languages with particular typological profiles, even across families. Common patterns include bias toward isolating/fusional morphology over agglutinative/polysynthetic, toward fixed word order over free, and toward phonologically simple over complex. These biases often reflect the dominance of Standard Average European features in NLP development.

Resource Bias

Resource bias is pervasive: samples disproportionately include languages with existing NLP resources, large digital corpora, or substantial linguistic documentation. While pragmatically understandable, this bias perpetuates inequality and limits our understanding of low-resource language processing.

Geographic Bias

Geographic bias manifests as under-representation of certain regions, particularly interior South America, Papua New Guinea, and parts of Africa and Australia. These regions include languages with unique typological profiles, so their exclusion reduces structural diversity as well.

Framework-Guided Resource Development

Beyond evaluation, the framework can guide resource development to maximize diversity gains. When deciding which languages to add to a corpus or benchmark, researchers can use diversity metrics to identify which additions would most increase representativeness.

For instance, suppose a benchmark currently includes English, Spanish, Chinese, Arabic, and Hindi. Computing diversity scores reveals low morphological diversity (all are isolating or fusional) despite reasonable genealogical diversity. The framework suggests prioritizing addition of morphologically complex languages: perhaps Turkish (agglutinative), Swahili (Bantu noun classes), or a polysynthetic language like Inuktitut.

This application exemplifies how quantitative diversity metrics enable principled resource allocation decisions, moving beyond ad hoc language selection.

Discussion

Theoretical Implications

The framework presented here bridges linguistic typology and multilingual NLP, demonstrating how typological knowledge can inform computational research design. This integration has implications for both fields.

For Linguistic Typology

Linguistic typology has long grappled with sampling bias in cross-linguistic studies (Bakker, 2010). While typologists recognize the importance of representative sampling, practical constraints often limit sample diversity. Our framework operationalizes typological diversity in ways that enable systematic bias assessment. Moreover, by connecting diversity metrics to computational outcomes, the framework demonstrates the practical consequences of typological variation—potentially motivating additional typological research on underexplored dimensions.

For Multilingual NLP

For NLP, the framework provides methodological grounding for diversity claims. Rather than relying on intuitive judgments, researchers can compute objective diversity scores, report them transparently, and compare samples systematically. This methodological advance should improve reproducibility and enable meta-analyses identifying which typological dimensions most affect model performance.

Furthermore, the framework highlights that “multilingual” and “typologically diverse” are not synonymous. Including many languages is insufficient if they cluster typologically. This realization should prompt reconsideration of how multilingual capabilities are evaluated and reported.

Practical Implementation Challenges

While conceptually sound, the framework faces practical implementation challenges that researchers must navigate.

Data Availability

Computing diversity metrics requires typological data, but coverage is incomplete. WALS includes ~2,600 languages but with substantial missing data for many features. PHOIBLE covers ~3,000 languages but with uneven documentation quality. For poorly documented languages, diversity assessment becomes necessarily approximate.

This limitation suggests two responses. First, researchers should acknowledge uncertainty in diversity assessments, reporting confidence intervals or ranges rather than point estimates. Second, the framework creates incentives for typological data collection, as more complete databases enable better diversity assessment.

Computational Costs

Increasing diversity, particularly by including low-resource languages, imposes computational and logistical costs. Developing resources for undocumented languages requires linguistic fieldwork, community collaboration, and substantial time investment. These costs can limit feasible diversity in practice.

However, computational costs and diversity are not necessarily zero-sum. Strategic sampling can maximize diversity within resource constraints. Moreover, some research questions require less diversity—studying morphological processing in agglutinative languages need not include all language families. The framework enables informed trade-offs rather than mandating maximal diversity universally.

Feature Selection and Weighting

The framework requires choosing which typological features to include and how to weight them (the and parameters). These choices affect resulting diversity scores, introducing potential arbitrariness.

We recommend two approaches. First, use task-relevant features when diversity requirements are task-specific. For morphological tasks, weight morphological features heavily; for semantic tasks, weight lexical features. Second, use equal weighting across dimensions absent specific justification, preventing cherry-picking of features that favor particular samples.

Limitations and Future Directions

Sociolinguistic Factors

The current framework focuses on structural and genealogical diversity, giving less attention to sociolinguistic variation. Yet language varieties, registers, and social contexts also constitute important diversity dimensions (Jørgensen et al., 2015). A speaker’s actual language use may differ substantially from the standardized variety represented in corpora. Future work should integrate sociolinguistic diversity metrics, considering variation along dimensions of formality, medium, social context, and speaker demographics.

Dynamic Language Change

Languages change over time, and typological properties can shift through contact, language learning, and internal innovation. The framework treats languages as static entities with fixed typological properties, neglecting diachronic dynamics. Incorporating temporal dimensions could better capture how diversity changes over time and how language contact affects typological profiles.

Dialect and Variety Diversity

Related to sociolinguistic factors, the framework currently treats languages as discrete units, ignoring within-language diversity. However, many languages exhibit substantial dialectal variation with typological consequences. Spanish varieties show morphological and phonological differences; Arabic varieties constitute nearly distinct languages. Future refinements should consider within-language diversity, potentially treating major varieties as separate sampling units.

Endangered Language Documentation

Many typologically unique languages are endangered, facing imminent extinction. The framework could be extended to prioritize endangered languages, balancing typological diversity with urgency for documentation. This would align linguistic research with language preservation efforts, creating synergies between theoretical, computational, and community-based work.

Ethical Considerations

Discussions of language diversity and resource allocation carry ethical implications that deserve explicit consideration.

Research Extractivism

Researchers must avoid extractive relationships with low-resource language communities, where data is collected without reciprocal benefits (Bird, 2020). Pursuing typological diversity should not mean treating speakers merely as data sources. Ethical multilingual research requires meaningful community engagement, benefit-sharing, and respect for linguistic sovereignty.

Representation Without Exploitation

Including diverse languages in research samples is necessary but insufficient for equity. Technology must be developed with and for communities, not merely about them. The framework identifies sampling gaps but does not resolve fundamental power asymmetries in who conducts research and who benefits.

Beyond Representation

Finally, we must recognize that diversity metrics, however sophisticated, cannot fully capture the value and complexity of human linguistic diversity. Languages are not merely collections of typological features but vehicles for culture, identity, and knowledge. Quantitative frameworks like ours provide useful tools but should complement rather than replace humanistic engagement with linguistic diversity.

Recommendations and Best Practices

Based on the framework and analysis presented, we propose the following best practices for multilingual research:

1. Report diversity metrics explicitly. Publications claiming typological diversity should report quantitative diversity scores across relevant dimensions, enabling readers to assess claims independently.
2. Justify language selection. Researchers should explain why particular languages were included, demonstrating how selections maximize relevant diversity or acknowledging constraints that limited diversity.
3. Distinguish diversity dimensions. Claims should specify what type of diversity is present (genealogical, morphological, etc.) rather than making blanket “diverse” claims.
4. Consider task-relevant diversity. Diversity requirements should match research questions. Morphological tasks require morphological diversity; semantic tasks require lexical diversity. Not all research requires maximal diversity across all dimensions.
5. Include low-resource languages. Unless research specifically targets high-resource scenarios, samples should include substantial low-resource representation to avoid perpetuating inequality.
6. Acknowledge limitations. Researchers should explicitly acknowledge sampling limitations, describing which types of diversity are lacking and how this affects generalizability.
7. Use stratified sampling. When building new resources, use stratified sampling that ensures representation across genealogical families, geographic regions, and typological categories.
8. Collaborate with communities. For low-resource and endangered languages, establish genuine collaborative relationships with speaker communities, ensuring reciprocal benefits.
9. Share diversity assessment tools. Researchers developing multilingual resources should release code for computing diversity metrics, facilitating systematic diversity assessment by others.
10. Iterate toward greater diversity. Resource development should be progressive, with successive versions expanding diversity along under-represented dimensions.

Implications for Funding and Review

The framework has implications beyond individual researchers, suggesting changes in how funding agencies and peer reviewers evaluate multilingual research.

Funding Priorities

Funding agencies should prioritize projects that increase linguistic diversity in NLP resources, particularly for underrepresented typological profiles and low-resource languages. Grant evaluation criteria should include diversity assessment, with higher scores for projects targeting diversity gaps. Moreover, agencies should fund typological database development, recognizing this infrastructure as essential for diversity-aware research.

Peer Review Standards

Reviewers should scrutinize diversity claims, expecting quantitative justification rather than accepting assertions at face value. When papers claim “typologically diverse” samples, reviewers should verify this using available metrics. Papers with limited diversity should not be rejected automatically but should acknowledge limitations and temper generalizability claims accordingly.

Conclusion

This article has presented a comprehensive framework for quantifying typological diversity in multilingual research. By operationalizing diversity across six core dimensions—phonological, morphological, syntactic, lexical, genealogical, and areal—and integrating resource availability considerations, the framework enables systematic assessment of language sample representativeness. Application of the framework to existing multilingual benchmarks reveals substantial and systematic biases toward Indo-European, high-resource, and structurally similar languages, even in resources claiming diversity.

These biases matter. They limit the scientific validity of generalizability claims, perpetuate technological inequality, and impede theoretical progress in understanding linguistic variation. Models trained on typologically narrow samples may appear to achieve cross-lingual success while actually exploiting hidden similarities. Low-resource languages with unique typological properties remain marginalized, their speakers excluded from technological benefits.

The framework presented here provides methodological tools to address these problems. By computing quantitative diversity scores, researchers can move beyond vague diversity claims to transparent, objective assessments. The metrics enable identification of specific biases—genetic, structural, resource-based, or geographic—facilitating targeted remediation. Framework-guided resource development can maximize diversity gains, ensuring that incremental additions address the most critical gaps.

Importantly, the framework bridges linguistic typology and multilingual NLP, demonstrating the practical value of typological knowledge for computational research. This integration benefits both fields: typology gains concrete applications and motivation for continued descriptive work, while NLP gains principled methods for language sampling and diversity assessment.

Nevertheless, significant challenges remain. Typological data coverage is incomplete, particularly for endangered and poorly documented languages. Increasing diversity often requires substantial resources for fieldwork, documentation, and community collaboration. The framework’s formal metrics cannot capture all dimensions of linguistic diversity’s value, nor can they resolve fundamental ethical questions about research relationships and power dynamics.

Despite these limitations, we believe the framework represents meaningful progress toward more rigorous, transparent, and equitable multilingual research. As multilingual NLP continues to grow, principled approaches to language sampling become increasingly essential. The alternative—continued reliance on ad hoc language selection and unsupported diversity claims—perpetuates existing biases and limitations.

Looking forward, we envision several developments. First, typological databases will continue to expand, enabling more comprehensive diversity assessment. Second, the research community will increasingly adopt quantitative diversity metrics, establishing new norms for reporting and evaluation. Third, funding agencies and institutions will recognize diversity as a priority, allocating resources to address identified gaps. Fourth, technological advances will reduce barriers to including low-resource languages, making diversity more achievable in practice.

Ultimately, the goal is not diversity for its own sake but genuine linguistic inclusivity—research that represents the full scope of human linguistic diversity, technology that serves all language communities, and theory that accounts for all of language’s richness. Quantifiable diversity metrics, as presented in this framework, provide essential tools for realizing this vision. By making diversity measurable, we make it achievable.

The path forward requires sustained commitment from researchers, institutions, and communities. It requires recognizing that multilingual NLP is not merely a technical challenge but a scientific and ethical imperative. It requires humility about the limitations of current methods and curiosity about the vast linguistic diversity we have yet to understand. Most fundamentally, it requires treating linguistic diversity not as an obstacle to overcome but as a precious resource to study, preserve, and celebrate.

This framework is offered as a contribution to that ongoing effort—a practical tool for advancing more diverse, more rigorous, and more equitable multilingual research. Its success will be measured not by the elegance of its mathematics but by its impact on actual research practice, by the degree to which it helps expand linguistic representation in NLP, and by its contribution to technology that serves all of the world’s languages and their speakers.

References