Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation

• URL: http://arxiv.org/abs/2408.12780v2
• Date: Thu, 3 Oct 2024 22:45:29 GMT
• Title: Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation
• Authors: Vivek Iyer, Bhavitvya Malik, Pavel Stepachev, Pinzhen Chen, Barry Haddow, Alexandra Birch,
• Abstract summary: We explore what it would take to adapt Large Language Models for low-resource languages. We show that parallel data is critical during both pre-training andSupervised Fine-Tuning (SFT) Our experiments with three LLMs across two low-resourced language groups reveal consistent trends, underscoring the generalizability of our findings.
• Score: 62.202893186343935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the recent popularity of Large Language Models (LLMs) in Machine Translation (MT), their performance in low-resource languages (LRLs) still lags significantly behind Neural Machine Translation (NMT) models. In this work, we explore what it would take to adapt LLMs for the low-resource setting. Particularly, we re-examine the role of two factors: a) the importance and application of parallel data, and b) diversity in Supervised Fine-Tuning (SFT). Recently, parallel data has seen reduced use in adapting LLMs for MT, while data diversity has been embraced to promote transfer across languages and tasks. However, for low-resource LLM-MT, we show that the opposite is true for both considerations: a) parallel data is critical during both pre-training and SFT; b) diversity tends to cause interference instead of transfer. Our experiments with three LLMs across two low-resourced language groups -- Indigenous American and North-East Indian -- reveal consistent trends, underscoring the generalizability of our findings. We believe these insights will be valuable for scaling to massively multilingual LLM-MT models that can effectively serve LRLs.

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