# Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?

arXiv: https://arxiv.org/abs/2505.16814 · HTML: https://mlsanity.com/paper/2505.16814/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 19 of 19)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier](https://mlsanity.com/paper/2412.04261/) (2024)
- [BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer](https://mlsanity.com/paper/2305.14857/) (2023)
- [Neural Cross-Lingual Named Entity Recognition with Minimal Resources](https://mlsanity.com/paper/1808.09861/) (2018)
- [MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition](https://mlsanity.com/paper/2210.12391/) (2022)
- [Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection](https://mlsanity.com/paper/1707.02483/) (2017)
- [NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data](https://mlsanity.com/paper/2402.15343/) (2024)
- [Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages](https://mlsanity.com/paper/2212.10168/) (2022)
- [Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark](https://mlsanity.com/paper/2311.09122/) (2023)
- [Toward More Meaningful Resources for Lower-resourced Languages](https://mlsanity.com/paper/2202.12288/) (2022)
- [Sources of Transfer in Multilingual Named Entity Recognition](https://mlsanity.com/paper/2005.00847/) (2020)
- [CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP](https://mlsanity.com/paper/2404.00415/) (2024)
- [What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis](https://mlsanity.com/paper/1909.03598/) (2019)
- [DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition](https://mlsanity.com/paper/2211.08104/) (2022)
- [Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields](https://mlsanity.com/paper/2404.09383/) (2024)

## Abstract (arXiv)

Named Entity Recognition(NER) for low-resource languages aims to produce robust systems for languages where there is limited labeled training data available, and has been an area of increasing interest within NLP. Data augmentation for increasing the amount of low-resource labeled data is a common practice. In this paper, we explore the role of synthetic data in the context of multilingual, low-resource NER, considering 11 languages from diverse language families. Our results suggest that synthetic data does in fact hold promise for low-resource language NER, though we see significant variation between languages.