  Bilingual Lexicon Induction (BLI), where words are translated between two
languages, is an important NLP task. While noticeable progress on BLI in rich
resource languages using static word embeddings has been achieved. The word
translation performance can be further improved by incorporating information
from contextualized word embeddings. In this paper, we introduce ProMap, a
novel approach for BLI that leverages the power of prompting pretrained
multilingual and multidialectal language models to address these challenges. To
overcome the employment of subword tokens in these models, ProMap relies on an
effective padded prompting of language models with a seed dictionary that
achieves good performance when used independently. We also demonstrate the
effectiveness of ProMap in re-ranking results from other BLI methods such as
with aligned static word embeddings. When evaluated on both rich-resource and
low-resource languages, ProMap consistently achieves state-of-the-art results.
Furthermore, ProMap enables strong performance in few-shot scenarios (even with
less than 10 training examples), making it a valuable tool for low-resource
language translation. Overall, we believe our method offers both exciting and
promising direction for BLI in general and low-resource languages in
particular. ProMap code and data are available at
\url{https://github.com/4mekki4/promap}.
