ELR-1000: A Community-Generated Dataset for Endangered Indic Indigenous Languages
This work addresses the need for benchmarks in underrepresented languages to advance equitable language technologies, though it is incremental in applying existing methods to new data.
The authors tackled the problem of low-resource language translation by creating a multimodal dataset of 1,060 traditional recipes in 10 endangered Indic languages, and found that providing targeted context significantly improved translation quality in large language models.
We present a culturally-grounded multimodal dataset of 1,060 traditional recipes crowdsourced from rural communities across remote regions of Eastern India, spanning 10 endangered languages. These recipes, rich in linguistic and cultural nuance, were collected using a mobile interface designed for contributors with low digital literacy. Endangered Language Recipes (ELR)-1000 -- captures not only culinary practices but also the socio-cultural context embedded in indigenous food traditions. We evaluate the performance of several state-of-the-art large language models (LLMs) on translating these recipes into English and find the following: despite the models' capabilities, they struggle with low-resource, culturally-specific language. However, we observe that providing targeted context -- including background information about the languages, translation examples, and guidelines for cultural preservation -- leads to significant improvements in translation quality. Our results underscore the need for benchmarks that cater to underrepresented languages and domains to advance equitable and culturally-aware language technologies. As part of this work, we release the ELR-1000 dataset to the NLP community, hoping it motivates the development of language technologies for endangered languages.