  Adapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning
techniques designed to make the training of language models more efficient.
Previous results demonstrated that these methods can even improve performance
on some classification tasks. This paper complements the existing research by
investigating how these techniques influence the classification performance and
computation costs compared to full fine-tuning when applied to multilingual
text classification tasks (genre, framing, and persuasion techniques detection;
with different input lengths, number of predicted classes and classification
difficulty), some of which have limited training data. In addition, we conduct
in-depth analyses of their efficacy across different training scenarios
(training on the original multilingual data; on the translations into English;
and on a subset of English-only data) and different languages. Our findings
provide valuable insights into the applicability of the parameter-efficient
fine-tuning techniques, particularly to complex multilingual and multilabel
classification tasks.
