Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages

Avijit Roy; Proma Roy

REVIEW 3 major objections 6 minor 26 references

Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages

T0 review · 3 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Structural barriers, not model quality, explain why AI serves Bengali speakers poorly.

desk verdict A clear, well-referenced synthesis of why Bengali speakers are structurally underserved by AI infrastructure; the four-barrier framing is useful, but the headline 67:1 token ratio compares different corpora and the tokenization-to-performance link is asserted, not shown. read the letter →

arxiv 2608.12278 v1 pith:6GNSEQ7K submitted 2026-08-12 cs.CL cs.AIcs.CY

Avijit Roy , Proma Roy This is my paper · ORCID

classification cs.CLcs.AIcs.CY

keywords low-resourcelanguagesBengaliNLPAIandlinguisticequitydigitaldivideoffline-firstdesignstructuralsilencetokenizationpenaltymultilingualmodelperformance

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that speakers of underrepresented languages are locked out of AI by four structural failures that exist before any model is trained: a web presence gap, a training-token deficit, a tokenization penalty, and a connectivity exclusion. Using Bengali as the case, it documents that Bengali accounts for under 0.5% of global web content despite roughly 4% of the world's population, that major multilingual corpora give it about 30 billion training tokens against roughly 2 trillion English tokens, that Bengali's alphasyllabary script—a writing system in which diacritics and conjunct forms attach to base characters—fragments into more subword tokens per word than English under standard tokenizers, and that rural Bangladesh has 36.5% individual internet penetration versus 71.4% in urban areas. The cumulative effect, the paper contends, is that uneven multilingual model performance reflects longstanding resource-allocation decisions and design defaults rather than model quality alone, so dataset scarcity should be treated as a structural barrier and offline-first design as an equity strategy.

What carries the argument

The mechanism that carries the argument is the compounding sequence of the four infrastructure layers, anchored by two quantitative measures: the $67{:}1$ training-token ratio and token fertility, defined as the average number of subword tokens required to represent one word or linguistic unit. Token fertility is the crucial converter: it turns the script difference between Latin-script English and Bengali's alphasyllabary script, with its diacritics (matras) and conjuncts (yuktakshar), into a computational penalty inside standard Byte Pair Encoding (BPE) and WordPiece tokenizers, so that even equal data volumes would not produce equal representational quality. The four failures are not independent; each one feeds the next, and the paper uses this cumulative structure to explain why Bengali performance lags across model families despite Bengali's inclusion in multilingual training sets.

What would settle it

Compute the English-to-Bengali token ratio inside a single corpus, such as within the Indic corpus the paper uses or within a matched web-crawl sample, and measure token fertility on aligned Bengali and English texts with the same tokenizer; if the within-corpus ratio falls far below $67{:}1$, or if fertility-adjusted Bengali tokens carry nearly the same per-token signal as English tokens, the quantitative core of the structural-silence argument would be contradicted.

Watch

Extended reading notes

Core claim

The paper's central claim is that the poor performance of general-purpose large language models on Bengali is not primarily a modeling shortcoming but the predictable output of what it calls structural silence: the accumulated weight of design decisions that never centered Bengali in AI infrastructure. It identifies four interlocking failures: a web presence gap (under 0.5% of web content for roughly 4% of the global population), a $67{:}1$ English-to-Bengali training-token deficit (Sangraha's $30$B Bengali allocation against Common Corpus's roughly $2$T English tokens), a tokenization penalty from Bengali's alphasyllabary script that raises token fertility under standard subword tokenizers, and a connectivity exclusion ($36.5\%$ rural versus $71.4\%$ urban individual internet penetration) that makes cloud-dependent tools functionally inaccessible to rural learners. Each failure compounds the others: less web presence means fewer tokens, fewer tokens plus higher token fertility means less usable representation, and cloud-based deployment assumes away the connectivity that would let users reach any web-scale model. The paper concludes that model performance gaps track training feasibility and resource allocation, and that offline-first, locally deployable AI is an equity-oriented infrastructure strategy rather than a degraded compromise.

Load-bearing premise

The argument's quantitative load rests on the $67{:}1$ English-to-Bengali token ratio, which compares Bengali's $30$ billion tokens in the Sangraha corpus with English's roughly $2$ trillion tokens in a different corpus, Common Corpus; if the true within-corpus ratio is much smaller, or if Bengali's higher token fertility is offset by other efficiencies, the claimed compounding deficit weakens.

Editorial extensions

If this is right

If the structural-silence account is right, improving multilingual model performance on Bengali requires changing infrastructure such as web content, tokenizers, benchmarks, and deployment assumptions, not just scaling up one model.
Equitable Bengali model performance will require substantially more than proportional training data, because the tokenization penalty means Bengali tokens carry less usable signal per token under standard subword tokenizers.
Offline-first, locally deployed models, made feasible by quantization and parameter-efficient fine-tuning, should be treated and funded as an equity strategy rather than as a degraded fallback.
Evaluation frameworks that test only high-connectivity cloud use validate tools that are inaccessible to rural learners, so benchmarks should include low-bandwidth and offline conditions.
Dataset construction, benchmark creation, and evaluation protocols for underrepresented languages deserve recognition as primary research contributions, not as supporting labor.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension the paper leaves implicit: recomputing the English-to-Bengali token ratio within a single aligned multilingual corpus would show whether the $67{:}1$ figure survives at the same order of magnitude, which would strengthen or qualify the compounding-deficit claim.
The same four-failure diagnosis likely applies, with different magnitudes, to other large languages with Indic or African scripts, such as Hindi, Tamil, or Amharic, where web presence and tokenizer fit are similarly skewed; a comparative case study would test whether structural silence is a general mechanism.
If the tokenization penalty is the binding constraint, then designing tokenizers that respect Bengali's orthographic units could reduce the data required for parity, effectively converting part of the $67{:}1$ deficit into a smaller gap; this is an intervention the paper motivates but does not test.
Field studies that log actual learner usage of cloud-based versus offline AI tutors in rural Bangladesh would directly test whether offline-first design changes learning outcomes, since the paper's connectivity argument predicts a large access effect that survey statistics alone do not demonstrate.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

The paper is a clear, well-written synthesis, not a new empirical study. Its contribution is the four-barrier structure—web presence, token deficit, tokenization penalty, connectivity exclusion—as an account of why Bengali speakers are underserved before any model is trained. That framing is genuinely useful, and the paper does it honestly: it explicitly says it is a case study and analytic synthesis, and it cites the underlying literature for each barrier.

What is new is the integration and the label 'structural silence,' plus the argument that dataset scarcity should be treated as structural rather than technical. The cognitive load tie-in and the offline-first-as-equity argument are reasonable and well-supported. For a reader who wants a compact account of why multilingual models underperform on Bengali, this is a solid overview.

The soft spots are real but addressable. The headline 67:1 ratio compares Bengali tokens in Sangraha with English tokens in Common Corpus—two different corpora. Footnote 1 concedes this, but the abstract and Section 3.2 present it as a measured fact, and the 'cumulative and compounding' claim leans on it. If the same-corpus ratio is materially smaller, the quantitative indictment weakens. The tokenization penalty is also asserted to degrade downstream performance without a direct causal measurement; the cited work supports higher fertility, but not the step from fertility to quality. That is a moderate concern, not a fatal one. The qualitative claim that Bengali is disadvantaged in data, tokenization, and deployment survives the ratio fix.

There is no invented data or overclaiming about novelty. The paper does what it says: it diagnoses and frames. It will not satisfy someone looking for new benchmarks, but it is a useful reference for educators, policymakers, and NLP researchers working on low-resource languages. I would send it to a serious referee, though the referee should push for a same-corpus token ratio or a clear hedge in the abstract, and for a more careful statement of the tokenization-to-performance link.

Referee Report

3 major / 6 minor

Summary. The paper examines AI infrastructure barriers for Bengali speakers, identifying four interlocking failures: a web presence gap, a training token deficit, a tokenization penalty, and connectivity exclusion. It argues that uneven multilingual model performance reflects structural design decisions rather than isolated technical limitations, and it concludes that offline-first design and dataset construction should be treated as equity-oriented research contributions. The contribution is framed as an analytic synthesis rather than a new benchmark or system.

Significance. If the qualitative argument is accepted, the paper provides a useful integrative account of how disparities at different layers of AI infrastructure compound. Its strength lies in linking published benchmarks, infrastructure statistics, and cognitive load theory into a single explanatory narrative, and in honestly stating that it introduces no new empirical data. The paper also gives explicit recognition to dataset construction as primary research and to offline-first design as equity architecture. However, its only original quantitative contribution, the 67:1 token ratio, is based on a cross-corpus comparison that the authors themselves concede is illustrative, which limits the force of the quantitative indictment.

major comments (3)

[Section 3.2 and Figure 2] The 67:1 training-token ratio is computed from two different corpora: the Bengali token allocation in Sangraha and the English token count in Common Corpus. Footnote 1 acknowledges this, but the abstract and the main text of Section 3.2 state the ratio as a quantitative fact ('The ratio ... is therefore approximately 67:1'). Because the headline claim of the paper rests on this ratio, the main text must either present the figure as an explicitly cross-corpus illustration or replace it with a same-corpus comparison from Sangraha or another single corpus; otherwise the 'cumulative and compounding' argument in Section 3.3 is built on an incomparable baseline.
[Section 3.3] The tokenization-penalty argument asserts that Bengali text requires 'significantly more subword tokens' than English, but no fertility ratio or token-count data are reported; the only citation is to Shahriar and Barbosa (2024), and footnote 2 says exact ratios depend on tokenizer and corpus. The claim that the penalty compounds the data deficit and that 'equitable Bengali model performance ... requires ... substantially more data' is therefore unsupported by a quantitative estimate. Please provide a concrete fertility comparison (e.g., tokens per word or per sentence for the relevant tokenizer) or explicitly downgrade this from a quantitative compounding effect to a qualitative structural tendency.
[Section 3.2] The sentence 'This ratio carries direct consequences for model performance' overstates the evidence: the cited evaluations (Kabir et al., 2024; Bhowmik et al., 2025) demonstrate that Bengali performance is lower, but they do not establish that the token ratio causes that gap, and the paper itself later describes the evidence as 'consistent with data-volume explanations.' Please rephrase to match the strength of the evidence, e.g., 'is associated with' or 'is consistent with,' to avoid a causal claim that the manuscript does not support.

minor comments (6)

[Introduction] There is a typo in the Introduction where 'Bengali is a revealing case study' appears as 'Bengaliisarevealingcasestudyprecisely' without spaces; please fix.
[Section 3.4] The cost dimension sentence has missing spaces: 'cost:The Daily Starreports' and 'The Business Standardreports' should be corrected.
[Figures] Figure 1 and Figure 2 are not explicitly referenced in the running text; add 'see Figure 1' and 'see Figure 2' at the appropriate points.
[References] Some references are incomplete, e.g., the IndicLLMSuite entry lists 'et al.' without a full author list, and the Sangraha corpus is cited via the IndicLLMSuite paper rather than a dedicated dataset description; please ensure the citation matches the resource name.
[Section 2.2] The sentence 'The gaps correlated with tokenization efficiency and model scale in ways consistent with data-volume explanations' is vague; specify the correlation measure or report the actual values.
[Abstract vs. Section 3.1] The abstract states 'roughly 285 million speakers' while Section 3.1 says 'approximately 242 million native speakers'; clarify which figure is used and why.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is an analytic synthesis that makes no predictions and fits no parameters.

full rationale

This paper is a case-study synthesis that assembles externally sourced statistics and published benchmark results; it derives no new quantitative result from its own assumptions. The one original quantitative figure, the 67:1 English–Bengali training-token ratio, is explicitly flagged in Footnote 1 as illustrative and cross-corpus ('These are not the same corpus, and the comparison is intended to illustrate the order-of-magnitude disparity... rather than an exact within-corpus measurement'), so although it is a comparability limitation, it is not circular: the ratio is not the output of a fitted model or of the paper's own definitions. The four 'failures' are documented from independent sources (web-content shares, tokenizer-fertility studies, connectivity surveys), and the cognitive-load argument is imported from established external literature rather than being constructed to match the conclusion. No parameters are fitted, no quantity is renamed as a prediction, and no load-bearing claim rests on a self-citation or on an author-imported uniqueness theorem. The paper's contribution is explicitly diagnostic and explanatory, and its central claim about structural exclusion remains independently supported by the cited external evidence even if that evidence is debatable.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper introduces no fitted parameters and no invented entities. The central claim rests on domain assumptions imported from prior literature, listed above; the most consequential are that web share proxies training-data share and that token fertility directly degrades downstream performance.

assumptions (4)

domain assumption Greater training data volumes generally produce better-performing models, and this relationship transfers across languages.
Invoked in Sections 2.1 and 3.2 to connect the token deficit to performance gaps, without direct causal evidence in this paper.
domain assumption A language's share of web content is a valid proxy for its representation in AI training corpora.
Section 3.1 states that training corpora are predominantly assembled from web crawls and that representation is constrained by online presence.
domain assumption Elevated token fertility for Bengali directly degrades model representation quality, so equal data volume would still leave a performance gap.
Section 3.3 asserts fragmentation 'disrupts the linguistic units that models rely on' and that substantially more data is required, but the cited work documents fertility ratios, not a quantitative causal link to downstream performance.
domain assumption Cognitive Load Theory, validated in classroom studies, applies to AI-generated explanations for programming education.
Section 4 extrapolates Roussel et al. (2017) and Soosai Raj et al. (2018) to AI-tutor contexts without learner data from this paper.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages." pith.science (2026). https://pith.science/paper/6GNSEQ7K

@misc{pith2026260812278,
  author       = {Pith},
  title        = {Pith review of: Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6GNSEQ7K}},
  note         = {Machine review of arXiv:2608.12278}
}

read the original abstract

Artificial intelligence tools for education and language support are increasingly framed as scalable responses to access gaps in under-resourced communities. Yet the infrastructure underlying these tools, including training corpora, tokenization schemes, evaluation benchmarks, and deployment architectures, can systematically disadvantage speakers of underrepresented languages before a model is trained. This paper examines these structural barriers through Bengali, one of the world's most widely spoken languages, focusing on AI-assisted education in low-connectivity environments. We identify four interlocking failures: a severe web presence gap, with Bengali accounting for less than 0.5% of global web content despite representing nearly 4% of the global population; a 67:1 training-token deficit between English and Bengali in major multilingual corpora; a tokenization penalty associated with Bengali's alphasyllabary script that compounds the data deficit through higher token fertility; and connectivity exclusion, with individual internet penetration at 36.5% in rural areas compared with 71.4% in urban areas. These failures reflect longstanding resource-allocation decisions, institutional priorities, and design defaults that did not center underrepresented languages in mainstream AI development. We argue that dataset scarcity should be understood as a structural barrier rather than an isolated technical limitation, and that offline-first design should be treated as an equity-oriented infrastructure strategy. We conclude with directions for linguistics and AI research aimed at reducing these structural inequalities.

Figures

Figures reproduced from arXiv: 2608.12278 by the authors.

**Figure 2.** The 67:1 English–Bengali training token gap. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Illustrative comparison of tokenization fertility between English and Bengali scripts. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Connectivity exclusion in rural versus urban Bangladesh. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

**Figure 5.** Figure 5: Illustration of the dual cognitive burden faced by learners processing technical content [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

26 extracted references · 23 canonical work pages

[1]

Sohel and Shahriyar, Rifat , booktitle =

Bhattacharjee, Abhik and Hasan, Tahmid and Islam, Wasi Ahmad and Mubasshir, Kazi Samin and Li, Yuan-Fang and Kang, Yong-Bin and Rahman, M. Sohel and Shahriyar, Rifat , booktitle =. 2022 , publisher =

work page 2022
[2]

Evaluating

Bhowmik, Shimanto and Dipto, Tawsif Tashwar and Islam, Md Sazzad and Hsu, Sheryl and Reasat, Tahsin , journal =. Evaluating. 2025 , url =

work page 2025
[3]

2025 , howpublished =

2025
[4]

arXiv preprint arXiv:2408.10441 , year =

Goldfish: Monolingual Language Models for 350 Languages , author =. arXiv preprint arXiv:2408.10441 , year =

arXiv
[5]

2025 , month = jan, day =

Internet Shows Stark Rural-Urban Divide , author =. 2025 , month = jan, day =

work page 2025
[6]

2023 , doi =

Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke , journal =. 2023 , doi =

work page 2023
[7]

The Teacher's Dilemma: Balancing Trade-Offs in Programming Education for Emergent Bilingual Students

The Teacher's Dilemma: Balancing Trade-Offs in Programming Education for Emergent Bilingual Students , author =. arXiv preprint arXiv:2506.14147 , year =

work page Pith review arXiv
[8]

2025 , howpublished =

What Are the Top 200 Most Spoken Languages? , author =. 2025 , howpublished =

work page 2025

Show all 26 references

[9]

Saiful and Mubasshir, Kazi and Li, Yuan-Fang and Kang, Yong-Bin and Rahman, M

Hasan, Tahmid and Bhattacharjee, Abhik and Islam, Md. Saiful and Mubasshir, Kazi and Li, Yuan-Fang and Kang, Yong-Bin and Rahman, M. Sohel and Shahriyar, Rifat , booktitle =. 2021 , publisher =

2021
[10]

and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu , booktitle =

Hu, Edward J. and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu , booktitle =. 2022 , url =

2022
[11]

2026 , url =

10 Most Spoken Languages in the World in 2026 , author =. 2026 , url =

2026
[12]

The State and Fate of Linguistic Diversity and Inclusion in the

Joshi, Pratik and Santy, Sebastin and Budhiraja, Amar and Bali, Kalika and Choudhury, Monojit , booktitle =. The State and Fate of Linguistic Diversity and Inclusion in the. 2020 , publisher =

2020
[13]

Saiful and Hoque, Enamul , booktitle =

Kabir, Mohsinul and Islam, Mohammed Saidul and Laskar, Md Tahmid Rahman and Nayeem, Mir Tafseer and Bari, M. Saiful and Hoque, Enamul , booktitle =. 2024 , url =

2024
[14]

2024 , publisher =

Khan, Mohammed Safiur Rahman and Madhani, Yash and Nilesh, Vinit and others , booktitle =. 2024 , publisher =

2024
[15]

Common Corpus: The Largest Collection of Ethical Data for

Langlais, Pierre-Jean and others , year =. Common Corpus: The Largest Collection of Ethical Data for
[16]

2024 , month = oct, day =

Cabinet Approves Conferring Status of Classical Language to. 2024 , month = oct, day =

2024
[17]

2024 , howpublished =

Reliably Exploring the Presence of Languages on the Internet , author =. 2024 , howpublished =. doi:10.32907/RO-139-5856605838 , url =

2024 doi
[18]

Mosiur and others , booktitle =

Raihan, Nafees and Jawad, Mohammad Abdullah and Rahman, Md. Mosiur and others , booktitle =. Overview of. 2025 , publisher =

2025
[19]

Bridging the Last Mile: Unpacking the Rural Digital Divide in

Rashed, Rayhan and Bhuyan, Muhammad Masroor Ali and others , booktitle =. Bridging the Last Mile: Unpacking the Rural Digital Divide in. 2025 , doi =

2025
[20]

Learning and Instruction , volume =

Learning Subject Content through a Foreign Language Should Not Ignore Human Cognitive Architecture: A Cognitive Load Theory Approach , author =. Learning and Instruction , volume =. 2017 , doi =

2017
[21]

Improving

Shahriar, Arif and Barbosa, Denilson , booktitle =. Improving. 2024 , url =

2024
[22]

Proceedings of the 49th

Does Native Language Play a Role in Learning a Programming Language? , author =. Proceedings of the 49th. 2018 , doi =

2018
[23]

2011 , doi =

Cognitive Load Theory , author =. 2011 , doi =

2011
[24]

2025 , month = apr, day =

52\. 2025 , month = apr, day =

2025
[25]

2026 , url =

International Mother Language Day , author =. 2026 , url =

2026
[26]

2026 , url =

Usage Statistics and Market Share of Content Languages for Websites, March 2026 , author =. 2026 , url =

2026

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

Sohel and Shahriyar, Rifat , booktitle =

Bhattacharjee, Abhik and Hasan, Tahmid and Islam, Wasi Ahmad and Mubasshir, Kazi Samin and Li, Yuan-Fang and Kang, Yong-Bin and Rahman, M. Sohel and Shahriyar, Rifat , booktitle =. 2022 , publisher =

work page 2022

[2] [2]

Evaluating

Bhowmik, Shimanto and Dipto, Tawsif Tashwar and Islam, Md Sazzad and Hsu, Sheryl and Reasat, Tahsin , journal =. Evaluating. 2025 , url =

work page 2025

[3] [3]

2025 , howpublished =

2025

[4] [4]

arXiv preprint arXiv:2408.10441 , year =

Goldfish: Monolingual Language Models for 350 Languages , author =. arXiv preprint arXiv:2408.10441 , year =

arXiv

[5] [5]

2025 , month = jan, day =

Internet Shows Stark Rural-Urban Divide , author =. 2025 , month = jan, day =

work page 2025

[6] [6]

2023 , doi =

Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke , journal =. 2023 , doi =

work page 2023

[7] [7]

The Teacher's Dilemma: Balancing Trade-Offs in Programming Education for Emergent Bilingual Students

The Teacher's Dilemma: Balancing Trade-Offs in Programming Education for Emergent Bilingual Students , author =. arXiv preprint arXiv:2506.14147 , year =

work page Pith review arXiv

[8] [8]

2025 , howpublished =

What Are the Top 200 Most Spoken Languages? , author =. 2025 , howpublished =

work page 2025