The Annotation Scarcity Paradox in Low-Resource NLP Evaluation: A Decade of Acceleration and Emerging Constraints
Over the past decade, low-resource NLP has grown explosively through cross-lingual transfer, multilingual models, and benchmark proliferation. However, the deep sociolinguistic expertise needed to evaluate generative systems is strained and inequitably distributed. This paper introduces the 'Annotation Scarcity Paradox,' reviews evaluation evolution from 2014, examines extractive pipelines, ghost work, and language data flaring, and discusses responses like data augmentation, model-based evaluation, participatory curation, and item response theory, assessing their equity and validity trade-offs.
[2605.19066] The Annotation Scarcity Paradox in Low-Resource NLP Evaluation: A Decade of Acceleration and Emerging Constraints
[Submitted on 18 May 2026]
Title:The Annotation Scarcity Paradox in Low-Resource NLP Evaluation: A Decade of Acceleration and Emerging Constraints
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Abstract:Over the past decade, low-resource natural language processing (NLP) has experienced explosive growth, propelled by cross-lingual transfer, massively multilingual models, and the rapid proliferation of benchmarks. Yet this apparent progress masks a critical, insufficiently examined tension: the deep sociolinguistic expertise required to evaluate increasingly complex generative systems is severely strained, inequitably distributed, and structurally marginalised. We present a critical narrative survey of low-resource NLP evaluation (2014--present), tracing its evolution across three phases: early heuristic optimism, the illusions of top-down benchmark scaling, and the current era of generative bottlenecks. We conceptualise the \emph{Annotation Scarcity Paradox}, the structural friction arising when the technical capacity to scale models vastly outpaces the sovereign human infrastructure required to authentically evaluate them. By examining extractive data pipelines, undercompensated ``ghost work'', and language data flaring, we argue that this paradox threatens the epistemic validity of reported progress. We survey emerging responses -- including data augmentation, model-based evaluation, participatory curation, and annotation-efficient approaches via item response theory and active learning -- and assess their equity and validity trade-offs. We close with a practitioner call to action, arguing that overcoming this bottleneck requires a paradigm shift from transactional data extraction to relational, community-embedded evaluation rooted in epistemic governance, data sovereignty, and shared ownership.
Comments: Under Review
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2605.19066 [cs.CL]
(or arXiv:2605.19066v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.19066
arXiv-issued DOI via DataCite (pending registration)
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From: Vukosi Marivate [view email] [v1] Mon, 18 May 2026 19:48:00 UTC (54 KB)
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