@inproceedings{r-etal-2025-field,
    title = "Field to Model: Pairing Community Data Collection with Scalable {NLP} through the {L}i{FE} Suite",
    author = "R, Karthick Narayanan  and
      Singh, Siddharth  and
      Singh, Saurabh  and
      Mathur, Aryan  and
      Kumar, Ritesh  and
      Ratan, Shyam  and
      Lahiri, Bornini  and
      Pareek, Benu  and
      Mathur, Neerav  and
      Gope, Amalesh  and
      Takhellambam, Meiraba  and
      Dawer, Yogesh",
    editor = "Le Ferrand, {\'E}ric  and
      Klyachko, Elena  and
      Postnikova, Anna  and
      Shavrina, Tatiana  and
      Serikov, Oleg  and
      Voloshina, Ekaterina  and
      Vylomova, Ekaterina",
    booktitle = "Proceedings of the Fourth Workshop on NLP Applications to Field Linguistics",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.fieldmatters-1.7/",
    pages = "76--84",
    ISBN = "979-8-89176-282-4",
    abstract = "We present LiFE Suite as a ``Field-to-Model'' pipeline, designed to bridge community-centred data collection with scalable language model development. This paper describes the various tools integrated into the LiFE Suite that make this unified pipeline possible. Atekho, a mobile-first data collection platform, is designed to empower communities to assert their rights over their data. MATra-Lab, a web-based data processing and annotation tool, supports the management of field data and the creation of NLP-ready datasets with support from existing state-of-the-art NLP models. LiFE Model Studio, built on top of Hugging Face AutoTrain, offers a no-code solution for building scalable language models using the field data. This end-to-end integration ensures that every dataset collected in the field retains its linguistic, cultural, and metadata context, all the way through to deployable AI models and archive-ready datasets."
}