Socially Responsible Data for Large Multilingual Language Models
<|reference_start|>Socially Responsible Data for Large Multilingual Language Models: Large Language Models (LLMs) have rapidly increased in size and apparent capabilities in the last three years, but their training data is largely English text. There is growing interest in multilingual LLMs, and various efforts are striving for models to accommodate languages of communities outside of the Global North, which include many languages that have been historically underrepresented in digital realms. These languages have been coined as "low resource languages" or "long-tail languages", and LLMs performance on these languages is generally poor. While expanding the use of LLMs to more languages may bring many potential benefits, such as assisting cross-community communication and language preservation, great care must be taken to ensure that data collection on these languages is not extractive and that it does not reproduce exploitative practices of the past. Collecting data from languages spoken by previously colonized people, indigenous people, and non-Western languages raises many complex sociopolitical and ethical questions, e.g., around consent, cultural safety, and data sovereignty. Furthermore, linguistic complexity and cultural nuances are often lost in LLMs. This position paper builds on recent scholarship, and our own work, and outlines several relevant social, cultural, and ethical considerations and potential ways to mitigate them through qualitative research, community partnerships, and participatory design approaches. We provide twelve recommendations for consideration when collecting language data on underrepresented language communities outside of the Global North.<|reference_end|>
@article{smart2024socially,
    title={Socially Responsible Data for Large Multilingual Language Models},
    author={Andrew Smart, Ben Hutchinson, Lameck Mbangula Amugongo, Suzanne
  Dikker, Alex Zito, Amber Ebinama, Zara Wudiri, Ding Wang, Erin van Liemt,
  Jo~ao Sedoc, Seyi Olojo, Stanley Uwakwe, Edem Wornyo, Sonja Schmer-Galunder,
  Jamila Smith-Loud},
    journal={ACM conference on Equity and Access in Algorithms, Mechanisms, and
  Optimization, 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.05247},
    primaryClass={cs.CL}
}