Self-Augmented In-Context Learning for Unsupervised Word Translation
<|reference_start|>Self-Augmented In-Context Learning for Unsupervised Word Translation: Recent work has shown that, while large language models (LLMs) demonstrate strong word translation or bilingual lexicon induction (BLI) capabilities in few-shot setups, they still cannot match the performance of 'traditional' mapping-based approaches in the unsupervised scenario where no seed translation pairs are available, especially for lower-resource languages. To address this challenge with LLMs, we propose self-augmented in-context learning (SAIL) for unsupervised BLI: starting from a zero-shot prompt, SAIL iteratively induces a set of high-confidence word translation pairs for in-context learning (ICL) from an LLM, which it then reapplies to the same LLM in the ICL fashion. Our method shows substantial gains over zero-shot prompting of LLMs on two established BLI benchmarks spanning a wide range of language pairs, also outperforming mapping-based baselines across the board. In addition to achieving state-of-the-art unsupervised BLI performance, we also conduct comprehensive analyses on SAIL and discuss its limitations.<|reference_end|>
@article{li2024self-augmented,
    title={Self-Augmented In-Context Learning for Unsupervised Word Translation},
    author={Yaoyiran Li, Anna Korhonen, Ivan Vuli'c},
    journal={arXiv preprint arXiv:2402.10024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10024},
    primaryClass={cs.CL cs.AI cs.IR cs.LG}
}