Bootstrapping Embeddings for Low Resource Languages
Embedding models are crucial to modern NLP. However, the creation of the most effective models relies on carefully constructed supervised finetuning data. For high resource languages, such as English, such datasets are readily available. However, for hundreds of other languages, they are simply non-existent. We investigate whether the advent of large language models can help to bridge this gap. We test three different strategies for generating synthetic triplet data used to optimise embedding models. These include in-context learning as well as two novel approaches, leveraging adapter composition and cross lingual finetuning of the LLM generator (XL-LoRA) respectively. We find that while ...
| Authors | Merve Basoz, Andrew Horne, Mattia Opper |
|---|---|
| Primary category | cs.CL |
| Published | 2 March 2026 |
| Last updated at source | 17 March 2026 |
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