  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.
