  We study model extraction attacks in natural language processing (NLP) where
attackers aim to steal victim models by repeatedly querying the open
Application Programming Interfaces (APIs). Recent works focus on limited-query
budget settings and adopt random sampling or active learning-based sampling
strategies on publicly available, unannotated data sources. However, these
methods often result in selected queries that lack task relevance and data
diversity, leading to limited success in achieving satisfactory results with
low query costs. In this paper, we propose MeaeQ (Model extraction attack with
efficient Queries), a straightforward yet effective method to address these
issues. Specifically, we initially utilize a zero-shot sequence inference
classifier, combined with API service information, to filter task-relevant data
from a public text corpus instead of a problem domain-specific dataset.
Furthermore, we employ a clustering-based data reduction technique to obtain
representative data as queries for the attack. Extensive experiments conducted
on four benchmark datasets demonstrate that MeaeQ achieves higher functional
similarity to the victim model than baselines while requiring fewer queries.
Our code is available at https://github.com/C-W-D/MeaeQ.
