Socially Responsible Data for Large Multilingual Language Models
Abstract
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 language inclusivity in LLMs, and various efforts are striving for models
to accommodate language communities outside of the Global North1
, which include many languages that have been historically
underrepresented digitally. 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.
data is largely English text. There is growing interest in language inclusivity in LLMs, and various efforts are striving for models
to accommodate language communities outside of the Global North1
, which include many languages that have been historically
underrepresented digitally. 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.
