Self-Augmented In-Context Learning for Unsupervised Word Translation
Yaoyiran Li Anna Korhonen Ivan Vulić
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.