# The Zeno's Paradox of `Low-Resource' Languages

arXiv: https://arxiv.org/abs/2410.20817 · HTML: https://mlsanity.com/paper/2410.20817/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 77 of its 175 references that are in the map.
- First posted on arXiv 28 Oct 2024.

## Builds on (most cited first, 30 of 77)

- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer](https://mlsanity.com/paper/2005.00052/) (2020)
- [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://mlsanity.com/paper/2402.07827/) (2024)
- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020)
- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://mlsanity.com/paper/2204.06487/) (2022)
- [Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System](https://mlsanity.com/paper/2109.14739/) (2021)
- [The Semantic Scholar Open Data Platform](https://mlsanity.com/paper/2301.10140/) (2023)
- [Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages](https://mlsanity.com/paper/2305.12182/) (2023)
- [UNKs Everywhere: Adapting Multilingual Language Models to New Scripts](https://mlsanity.com/paper/2012.15562/) (2020)
- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://mlsanity.com/paper/2205.02022/) (2022)
- [WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models](https://mlsanity.com/paper/2112.06598/) (2021)
- [UDapter: Language Adaptation for Truly Universal Dependency Parsing](https://mlsanity.com/paper/2004.14327/) (2020)
- [ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora](https://mlsanity.com/paper/2012.15674/) (2020)
- [Generalized Data Augmentation for Low-Resource Translation](https://mlsanity.com/paper/1906.03785/) (2019)
- [Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation](https://mlsanity.com/paper/2009.09359/) (2020)
- [Entity Linking in 100 Languages](https://mlsanity.com/paper/2011.02690/) (2020)
- [Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation](https://mlsanity.com/paper/2005.04816/) (2020)
- [MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset](https://mlsanity.com/paper/2010.04480/) (2020)
- [Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor`ub'a and Twi](https://mlsanity.com/paper/1912.02481/) (2019)
- [KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi](https://mlsanity.com/paper/2010.12174/) (2020)
- [Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation](https://mlsanity.com/paper/2305.10951/) (2023)
- [Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks](https://mlsanity.com/paper/2210.14712/) (2022)
- [Overlap-based Vocabulary Generation Improves Cross-lingual Transfer Among Related Languages](https://mlsanity.com/paper/2203.01976/) (2022)
- [Long Document Summarization in a Low Resource Setting using Pretrained Language Models](https://mlsanity.com/paper/2103.00751/) (2021)
- [AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages](https://mlsanity.com/paper/2109.04715/) (2021)
- [ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer](https://mlsanity.com/paper/2305.12708/) (2023)
- [Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages](https://mlsanity.com/paper/2001.11453/) (2020)
- [Simulated Multiple Reference Training Improves Low-Resource Machine Translation](https://mlsanity.com/paper/2004.14524/) (2020)
- [MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning](https://mlsanity.com/paper/2104.07908/) (2021)

## Built on by (most cited first, 12 of 12)

- [The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating It](https://mlsanity.com/paper/2505.24119/) (2025)
- [One ruler to measure them all: Benchmarking multilingual long-context language models](https://mlsanity.com/paper/2503.01996/) (2025)
- [Tiny Aya: Bridging Scale and Multilingual Depth](https://mlsanity.com/paper/2603.11510/) (2026)
- [Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and Beyond](https://mlsanity.com/paper/2503.07501/) (2025)
- [Low-resource Machine Translation: what for? who for? An observational study on a dedicated Tetun language translation service](https://mlsanity.com/paper/2411.12262/) (2024)
- [Steering LLMs toward Korean Local Speech: Iterative Refinement Framework for Faithful Dialect Translation](https://mlsanity.com/paper/2511.06680/) (2025)
- [NaijaNLP: A Survey of Nigerian Low-Resource Languages](https://mlsanity.com/paper/2502.19784/) (2025)
- [What Makes a Good Doctor Response? A Study on Text-Based Telemedicine](https://mlsanity.com/paper/2602.17194/) (2026)
- [Which Are the Low-Resource Languages of the Semantic Web?](https://mlsanity.com/paper/2605.05929/) (2026)
- [In Data or Invisible: Toward a Better Digital Representation of Low-Resource Languages with Knowledge Graphs](https://mlsanity.com/paper/2605.05931/) (2026)
- [Beyond Catalogue Counts: the Dataset Visibility Asymmetry in Low-Resource Multilingual NLP](https://mlsanity.com/paper/2605.17442/) (2026)
- [Sometin Beta Pass Notin: Improving Multilingual ASR for Nigerian Languages via Knowledge Distillation](https://mlsanity.com/paper/2605.17710/) (2026)

## Abstract (arXiv)

The disparity in the languages commonly studied in Natural Language Processing (NLP) is typically reflected by referring to languages as low vs high-resourced. However, there is limited consensus on what exactly qualifies as a `low-resource language.' To understand how NLP papers define and study `low resource' languages, we qualitatively analyzed 150 papers from the ACL Anthology and popular speech-processing conferences that mention the keyword `low-resource.' Based on our analysis, we show how several interacting axes contribute to `low-resourcedness' of a language and why that makes it difficult to track progress for each individual language. We hope our work (1) elicits explicit definitions of the terminology when it is used in papers and (2) provides grounding for the different axes to consider when connoting a language as low-resource.