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arxiv: 2605.17442 · v1 · pith:X2EWY6UZnew · submitted 2026-05-17 · 💻 cs.CL · cs.AI· cs.IR

Beyond Catalogue Counts: the Dataset Visibility Asymmetry in Low-Resource Multilingual NLP

Zhiyin Tan , Changxu Duan This is my paper

Pith reviewed 2026-05-20 13:03 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.IR
keywords multilingual NLPdataset visibilitylow-resource languagesresource density indexcitation miningdata scarcitydocumentation gaps
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The pith

Catalogue counts miss substantial dataset activity for many widely spoken languages.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper challenges the common practice of judging language resource levels solely by counts in centralized catalogues such as the LRE Map and Linguistic Data Consortium. It computes a Resource Density Index for the 200 most spoken languages and identifies 141 with zero or near-zero catalogued datasets per million speakers. An LLM-assisted search through research literature then locates 609 unique datasets tied to 53 of those languages, with hundreds remaining publicly accessible via working links. If correct, this means current scarcity rankings reflect documentation gaps at least as much as actual data production shortfalls, changing how researchers decide which languages need new collection efforts. The work therefore reframes multilingual data problems as questions of visibility and long-term accessibility rather than production alone.

Core claim

Among the 200 most widely spoken languages, 141 show an average Resource Density Index of zero or below 0.1 in major catalogues; literature mining over Semantic Scholar identifies 609 distinct datasets for 53 of these languages after validation, revealing that catalogue records alone substantially understate actual dataset circulation and reuse.

What carries the argument

The Resource Density Index, defined as the number of catalogued datasets divided by one million speakers, paired with an LLM-assisted citation-mining pipeline that extracts and consolidates dataset mentions from research papers.

If this is right

  • Language resource rankings used for funding or tool-building should combine catalogue data with literature evidence to avoid systematic underestimation.
  • Many languages currently labeled low-resource may already possess reusable datasets that simply lack central registration.
  • Efforts to reduce multilingual data scarcity must address long-term discoverability and link maintenance, since only 356 of the 609 identified datasets had working public access.
  • Standard scarcity metrics can misdirect collection priorities toward languages that already circulate data informally.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Automated literature-scanning tools could be run periodically to keep dataset inventories current for low-visibility languages.
  • The same visibility gap may exist in other research fields that rely on catalogue-style registries rather than citation patterns.
  • Prioritizing link preservation and metadata standards for newly found datasets could increase their effective reuse rate.

Load-bearing premise

The citation-mining process plus manual checks accurately locates and counts only genuine, relevant datasets without large numbers of false positives or overlooked items across the 141 languages.

What would settle it

A full manual audit of papers mentioning one of the 141 languages finds no additional datasets beyond the catalogue baseline.

Figures

Figures reproduced from arXiv: 2605.17442 by Changxu Duan, Zhiyin Tan.

Figure 1
Figure 1. Figure 1: Distribution of average catalogue-based RDI across 200 high-population languages. The distribution is heavily skewed toward zero, with 118 languages (59%) having no listed dataset in either catalogue, and a further 23 (11.5%) fall below 0.1. Find details in the GitHub repository. documented in the literature but are either no longer retrievable online or available only through gated channels. This distinct… view at source ↗
Figure 2
Figure 2. Figure 2: Aggregate trends in dataset emergence (release year) and subsequent usage (citation year). Use typically trails emergence by one to two years. terms of whether datasets exist, but also in terms of whether they are visible and discoverable through commonly used documentation channels. In sev￾eral cases, resources produced and reused within regional research communities, for example, in In￾donesia, India, an… view at source ↗
Figure 3
Figure 3. Figure 3: Summary of tasks, modalities, and languages for the 609 discovered datasets, showing the flow [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
read the original abstract

Multilingual NLP often relies on dataset counts from centralized catalogues to characterize which languages are resource-rich or resource-poor. However, these catalogues record only one layer of dataset visibility: what has been registered or institutionally distributed. They do not necessarily reflect which datasets are created, cited, or reused in the research literature. To examine this gap, we combine a catalogue-based baseline with literature-backed evidence of dataset circulation. We introduce the Resource Density Index (RDI), defined as the number of catalogued datasets per one million speakers, and compute it for the 200 most widely spoken languages in Ethnologue. Among them, 118 languages (59%) have an average RDI of zero across the LRE Map and the Linguistic Data Consortium (LDC), and another 23 fall below 0.1, corresponding to at most one catalogued dataset per ten million speakers. We then apply an LLM-assisted citation-mining pipeline over the Semantic Scholar corpus to these 141 low-visibility languages. After manual validation and consolidation, we identify 609 unique datasets across 53 languages, of which 356 remain openly accessible through working public links. These results reveal a substantial visibility gap: many large-speaker languages appear data-poor in catalogue records yet show clear evidence of dataset activity in the research literature. Our findings suggest that multilingual data scarcity should be understood not only as a production problem, but also as a question of documentation, discoverability, and long-term accessibility. Code and data are publicly available at (https://github.com/zhiyintan/dataset-visibility-asymmetry).

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper claims that centralized catalogues underestimate dataset availability for many low-resource languages in multilingual NLP. By defining the Resource Density Index (RDI) as catalogued datasets per million speakers for the 200 most widely spoken languages, they identify 141 languages with low RDI. An LLM-assisted citation-mining pipeline over Semantic Scholar, followed by manual validation, uncovers 609 unique datasets for 53 of these languages, with 356 having working public links, indicating a substantial visibility gap between catalogue records and research literature activity.

Significance. If the results are robust, this work makes a significant contribution by demonstrating that data scarcity in multilingual NLP is not solely a production issue but also one of discoverability and documentation. The public availability of code and data at the provided GitHub repository is a notable strength, supporting reproducibility and further research. It challenges reliance on catalogue counts alone for assessing language resources.

major comments (2)
  1. The manuscript describes an LLM-assisted pipeline over Semantic Scholar followed by manual validation to arrive at the 609 datasets figure, but does not report the sampling fraction validated, inter-annotator agreement, explicit relevance criteria, or false-positive/false-negative rates. This is a load-bearing issue for the central claim of a visibility gap, as the accuracy of these counts cannot be fully assessed without such details.
  2. The consolidation process for identifying 'unique datasets' and handling duplicates across the 141 languages is not detailed, including how working links were verified for the 356 accessible datasets. This affects the reliability of the reported numbers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive evaluation of our work's significance and for the constructive feedback on methodological transparency. We address each major comment below and commit to revisions that will strengthen the paper without altering its core claims.

read point-by-point responses

  1. Referee: The manuscript describes an LLM-assisted pipeline over Semantic Scholar followed by manual validation to arrive at the 609 datasets figure, but does not report the sampling fraction validated, inter-annotator agreement, explicit relevance criteria, or false-positive/false-negative rates. This is a load-bearing issue for the central claim of a visibility gap, as the accuracy of these counts cannot be fully assessed without such details.

    Authors: We agree that these validation details are essential for readers to evaluate the robustness of the 609-dataset count. The original manuscript summarized the pipeline and manual validation at a high level, focusing on the visibility gap findings rather than exhaustive methodological metrics. We will add a new subsection in the Methods section that reports the sampling fraction of LLM outputs subjected to manual review, the inter-annotator agreement achieved during validation, the explicit relevance criteria used by annotators, and the observed false-positive and false-negative rates from the validated sample. These additions will directly support the reliability of our central claim. revision: yes

  2. Referee: The consolidation process for identifying 'unique datasets' and handling duplicates across the 141 languages is not detailed, including how working links were verified for the 356 accessible datasets. This affects the reliability of the reported numbers.

    Authors: We acknowledge that the processes for deduplication and link verification were described too briefly. In the revised manuscript we will expand the relevant Methods paragraph to specify how unique datasets were identified (including normalization of titles, descriptions, and source papers, plus similarity-based duplicate removal), the criteria applied when consolidating across languages, and the exact procedure used to verify the 356 working public links (automated status checks followed by manual confirmation of accessibility and content relevance). This will improve reproducibility and address concerns about the reported numbers. revision: yes

Circularity Check

0 steps flagged

No circularity: direct empirical counts from external sources

full rationale

The paper defines the Resource Density Index (RDI) as a simple ratio of catalogued datasets to speakers using external catalogues (LRE Map, LDC, Ethnologue). It then applies an LLM-assisted mining pipeline over Semantic Scholar followed by manual validation to count additional datasets for low-RDI languages. These are straightforward measurement steps against external corpora; the headline result (609 datasets, visibility gap) is the direct output of those counts and does not reduce to any fitted parameter, self-referential definition, or self-citation chain. No equations, predictions, or uniqueness theorems appear. The work is self-contained empirical reporting with public code and data.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The work relies on standard external sources for speaker counts and catalogue data plus a new index; no free parameters are fitted to produce the headline counts.

axioms (2)
  • domain assumption Ethnologue speaker population figures are sufficiently accurate for normalizing dataset counts across languages.
    Used to compute RDI for the 200 most widely spoken languages.
  • domain assumption LRE Map and LDC constitute the main centralized records of registered datasets.
    Serve as the explicit baseline for identifying low-visibility languages.
invented entities (1)
  • Resource Density Index (RDI) no independent evidence
    purpose: Normalize catalogued dataset counts by speaker population to compare visibility across languages.
    New metric defined and applied in the paper.

pith-pipeline@v0.9.0 · 5814 in / 1331 out tokens · 59895 ms · 2026-05-20T13:03:59.602278+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

14 extracted references · 14 canonical work pages · 1 internal anchor

  1. [1]

    Introduction Linguistic datasets are a central part of multilingual natural language processing (NLP). They shape which languages can be modeled, benchmarked, and evaluated, and therefore influence which lan- guages are most visible in deployed language tech- nologies (Paullada et al., 2021; Blasi et al., 2022). Over the past decade, the field has investe...

    work page internal anchor Pith review Pith/arXiv arXiv 2021

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    A population-normalized view of catalogue visibility.We introduce the Resource Den- sity Index (RDI), a transparent metric for com- paringcatalogue-documenteddatasetsacross the 200 most widely spoken languages. By combining demographic information fromEth- nologuewithentriesfromtheLREMapandthe LDC, RDI makes it possible to compare doc- umentation density ...

    work page

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    The resulting inventory is manually validated, deduplicated, and en- riched with accessibility metadata, providing a usage-centered complement to catalogue- based documentation

    A citation-validated audit of dataset circu- lation.We adapt a citation-based dataset dis- covery framework to construct a language-by- language inventory of datasets evidenced in the research literature. The resulting inventory is manually validated, deduplicated, and en- riched with accessibility metadata, providing a usage-centered complement to catalo...

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    Evidence for visibility and accessibility gaps.By comparing catalogue-based RDI estimates with citation-grounded evidence of datasets cited, described, or reused in the lit- erature, we show that catalogue visibility and research circulation often diverge: many lan- guages with zero or near-zero catalogue pres- ence nonetheless have datasets documented an...

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    low-resource

    Related Work Catalogue infrastructures and resource visibil- ity.Studies of multilingual resource availability often rely on large cataloguing infrastructures that document and index language resources. System- atic documentation has long been a central goal of the language resources and evaluation community. The LRE Map (Calzolari et al., 2010; Del Gratt...

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    To do so, we combine two complementary views

    Methodology Our goal is to characterize multilingual dataset vis- ibility in a way that is comparable across large language communities, grounded in documented evidence, and sensitive to the gap between cata- logue records and research circulation. To do so, we combine two complementary views. First, we construct a population-normalized baseline from two ...

    work page 2024

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    We first examine how catalogue-based RDI values are distributed across the 200 languages in our comparison set (Sec- tion 4)

    Results and Analysis This section presents our empirical findings on mul- tilingual dataset visibility. We first examine how catalogue-based RDI values are distributed across the 200 languages in our comparison set (Sec- tion 4). We then compare catalogue-based es- timates with citation-based evidence of datasets appearing in the research literature (Sect...

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    low- resource

    Conclusion This study revisits how multilingual NLP conceptu- alizes low-resource status. Rather than equating scarcity with the absence of data, we examine how the visibility of datasets is shaped by documenta- tion practices and research circulation. To do so, we combine two complementary per- spectives. First,weintroducetheResourceDensity Index (RDI), ...

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    Future Work Future work will extend this framework beyond the low-visibility segment examined here to languages with higher catalogue RDIs. A preliminary run over the full set of 200 languages retrieves 7,299 candi- date dataset mentions prior to manual validation, indicating that a substantial portion of the multilin- gual dataset landscape remains to be...

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    Our dataset discovery process, while effective, has inherent constraints that may lead to an underestimation of the true resource landscape

    Limitations While our study reveals substantial asymmetries in multilingual dataset visibility, we acknowledge several limitations in our methodology and scope that offer avenues for future research. Our dataset discovery process, while effective, has inherent constraints that may lead to an underestimation of the true resource landscape. Our retrieval st...

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    HybrInt - Hybrid Intelligence through Interpretable AI in Machine Perception and Interaction

    Acknowledgment Zhiyin Tan was funded by the “HybrInt - Hybrid Intelligence through Interpretable AI in Machine Perception and Interaction” project (Zukunft Nds, Niedersächsisches Ministerium für Wissenschaft, Grant ID: ZN4219). Changxu Duan was funded by the InsightsNet project (funded by the Federal Ministry of Ed- ucation and Research (BMBF) under grant...

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    Bibliographical References Akari Asai, Eunsol Choi, Jonathan H. Clark, Jun- jie Hu, Chia-Hsuan Lee, Jungo Kasai, Shayne Longpre, Ikuya Yamada, and Rui Zhang, editors. 2022.Proceedings of the Workshop on Multilin- gual Information Access (MIA). Association for Computational Linguistics, Seattle, USA. Neetika Bansal, Dr. Vishal Goyal, and Dr. Simpel Rani. 2...

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    Data and its (dis)contents: A survey of dataset development and use in machine learn- ing research.Patterns, 2(11):100336. QwenTeam.2024. Qwen2: AFamilyofStrongand General Open-Source Language Models. SurangikaRanathungaandNisansadeSilva.2022. Some languages are more equal than others: Probing deeper into the linguistic disparity in the NLP world. InProce...

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