Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs
Pre-trained Large Language Model (LLMs) are effective on many tasks in zero-shot and few-shot settings.
ProofPaper ↗
Key points
- For machine translation of low-resource languages, the obvious options are few-shot prompting and fine-tuning on a small parallel set.
- We ask: what if moderate amounts of low-resource monolingual data are also available?
- We argue that, rather than continued pretraining, a more effective use of this data is the pre-LLM era technique of back-translation (BT).
- Across five low-resource languages, with Hindi as a high-resource control, and two 3B backbones, BT improves translation into the low-resource language by 16 to 173% XCOMET-XXL over parallel-only fine-tuning.
Sources (1)
- [1]Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 05:50 PM
Pre-trained Large Language Model (LLMs) are effective on many tasks in zero-shot and few-shot settings.
For machine translation of low-resource languages, the obvious options are few-shot prompting and fine-tuning on a small parallel set.
Extractive summary: sentences quoted from the sources.
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