@inproceedings{panahi-etal-2026-synthllm,
    title = "{S}ynth{LLM}: An {LLM}-based Scalable Synthetic Data Generation Pipeline for Low-Resource Languages",
    author = "Panahi, Solmaz  and
      Nedumpozhimana, Vasudevan  and
      Kelleher, John",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/cawl-year/2026.lrec-1.844/",
    doi = "10.63317/36i5afj23ivf",
    pages = "10776--10791",
    abstract = "Large Language Models (LLMs) have enabled scalable synthetic data generation, yet their effective adaptation to low-resource languages remains underexplored. We introduce an LLM-based generate and annotate paradigm to create synthetic datasets for low-resource NLP classification tasks. The framework employs a smaller model for text generation and a stronger model for automatic annotation. Using Farsi Natural Language Inference (NLI) as a case study, we construct a large-scale synthetic dataset of 100,000 labeled instances. We provide a systematic empirical analysis of annotation quality, label-distribution effects, and training regimes. We compare GPT-4o-mini, Aya-23-35B, and DeBERTa as annotators and examine how annotation variability propagates to downstream performance. Our results show that a warm-up phase with synthetic data consistently outperforms data mixing and reversed ordering. Notably, open-source annotation (Aya-23-35B) achieves comparable downstream performance to the proprietary model (GPT-4o-mini), with significant cost implications for deploying pipelines in low-resource settings. The dataset and code are publicly available at \url{https://huggingface.co/datasets/Solmazp/text2entail}."
}