How big is the low-resource language problem, and what can synthetic data actually do?
Low-resource languages—those with limited digital text—lag far behind English in AI support because models simply haven't seen enough high-quality examples. This isn't a niche issue: billions of people speak these languages, and the gap affects everything from search to translation to education. Synthetic data—text generated by AI models rather than collected from human sources—offers a way to fill this gap quickly and cheaply. The evidence here shows it's not just a stopgap; it can be a genuine equalizer. For instance, a 2025 study built a high-performing Urdu-English model using a synthetic dataset, achieving better results than several leading multilingual models—all within a training budget of under $100 [2]. That's a dramatic cost reduction compared to the millions typically spent on data collection and training.
The scale of the opportunity is also visible in pretraining. A 2026 study created a 540-billion-token synthetic dataset for 10 Indic languages, showing that synthetic data can be generated at massive scale [7]. Another 2026 study found that even small amounts of synthetic data enabled smaller models to outperform the large generator itself, particularly in low-resource languages [5]. This suggests that synthetic data isn't just a filler—it can actually produce better-performing models than the original data source, because it can be tailored to specific tasks and languages.
What are the specific ways synthetic data improves low-resource language models?
One key mechanism is knowledge injection: translating high-quality English data into low-resource languages during pretraining. A 2026 study introduced SynTrans, which does exactly this, and found it significantly improved performance on knowledge-intensive tasks, though it sometimes hurt fluency [1]. The same study also showed that synthetic data can be used to filter noisy real-world data—using synthetic examples as a guide to rank and select the best human-written text—matching or even surpassing human expert filtering [1]. This means synthetic data can help clean up the messy, low-quality data that often plagues low-resource languages.
Another mechanism is targeted weakness correction. A 2026 study developed a framework called HOTFIXR that probes a model's multilingual weaknesses and generates data specifically to fix them. It improved in-distribution performance by 6.2% on average, and reduced catastrophic forgetting on out-of-distribution languages by 7.1% [4]. This is crucial because it shows synthetic data can be used not just to add volume, but to address specific gaps in a model's abilities. Similarly, a 2025 study on Korean found that synthetic data, when carefully curated, could sustain long-horizon pretraining without model collapse, achieving performance comparable to open-weight multilingual baselines [3]. This directly counters the fear that synthetic data might degrade model quality over time.
What are the catches and challenges?
Synthetic data isn't a magic bullet—quality and cultural nuance matter. A 2026 study on Indic languages found that translations of English content often lacked cultural relevance, and that native generation in the target language was sometimes better [7]. This echoes the earlier finding that SynTrans improved knowledge but hurt fluency [1]. So, while synthetic data can inject knowledge, it may not automatically produce natural, culturally appropriate text. The solution seems to be careful curation and mixing with real data, as demonstrated by the Urdu model that used a modified self-instruct technique to incorporate cultural relevance and safety alignments [2].
Another challenge is safety and bias. A 2025 study on multilingual guardrails used a two-player reinforcement learning framework to generate synthetic safety data, achieving a nearly 10% improvement over a state-of-the-art model on English benchmarks while being 4.5x faster [8]. This shows synthetic data can help address safety gaps, but it also highlights that without careful design, synthetic data could perpetuate or even amplify biases. Finally, the effectiveness of synthetic data can depend on the typological distance between languages—a 2026 study on grammatical error detection found that zero-shot transfer performance varied with how closely related the source and target languages were [6]. So, synthetic data works best when the source and target languages share some structural similarities.
About These Sources
This answer is built on 8 studies (3 peer-reviewed, 5 preprints) — published from 2025 to 2026, 8 from 2024 or later — selected as the most relevant from 9 studies that passed quality screening, drawn from 32 papers retrieved from a database of over 500 million.
Sources used in this answer
How Can Synthetic Data Improve Multilingual Language Model Pretraining? A Data Quality Perspective
Proposed SynTrans (translating English data) and SynRank (using synthetic data to filter noisy real data); SynRank matched or surpassed human expert filtering and improved knowledge-intensive task performance, with higher filtering rates even improving performance with less data.
Alif: Advancing Urdu Large Language Models via Multilingual Synthetic Data Distillation
Developed Alif-1.0-8B-Instruct, a Urdu-English model trained on a synthetic dataset (Urdu-Instruct) using a modified self-instruct technique, outperforming several larger multilingual models on Urdu tasks within a training budget of under $100.
KORMo: Korean Open Reasoning Model for Everyone
Trained KORMo-10B, a 10.8B-parameter Korean-English model from scratch with 68.74% synthetic Korean data, achieving performance comparable to open-weight multilingual baselines and demonstrating that synthetic data can sustain long-horizon pretraining without model collapse.
LLMs Get Smarter from Targeted Synthetic Multilingual Data
Introduced HOTFIXR, a data generation framework that probes a student model's multilingual weaknesses and generates targeted synthetic data, improving in-distribution performance by 6.2%, reducing catastrophic forgetting on out-of-distribution tasks by 3.7%, and on out-of-distribution languages by 7.1%.
Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual Classification
Used a state-of-the-art multilingual LLM to generate synthetic datasets for 11 languages and 4 classification tasks, finding that even small amounts of synthetic data enabled smaller models to outperform the large generator itself, particularly in low-resource languages.
Typological Distance and Zero-Shot Grammatical Error Detection Performance
Investigated zero-shot grammatical error detection using synthetic error corpora generated by multilingual LLMs, finding that performance (F1-score) varied with the typological distance between source and target languages.
BhashaKritika: Building Synthetic Pretraining Data at Scale for Indic Languages
Constructed BhashaKritika, a 540B-token synthetic pretraining dataset for 10 Indic languages using 5 techniques, and found that grounding generation in documents, personas, and topics, as well as language choice, affected data quality; also introduced a modular quality evaluation pipeline.
DuoGuard: A Two-Player RL-Driven Framework for Multilingual LLM Guardrails
Proposed DuoGuard, a two-player RL framework where a generator and guardrail model co-evolve to produce synthetic safety data, achieving nearly 10% improvement over LlamaGuard3 (8B) on English benchmarks while being 4.5x faster with a 0.5B model, and substantially advancing multilingual safety tasks.