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Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article

Olesya Razuvayevskaya1, Ben Wu1, João A Leite1

  • 1Department of Computer Science, The University of Sheffield, Sheffield, United Kingdom.

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This summary is machine-generated.

Parameter-efficient fine-tuning techniques like Adapters and Low-Rank Adaptation (LoRA) offer efficient language model training. This study shows their effectiveness in multilingual text classification, even with limited data.

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Computational Linguistics

Background:

  • Parameter-efficient fine-tuning (PEFT) methods, including Adapters and Low-Rank Adaptation (LoRA), enhance language model training efficiency.
  • Prior research suggests PEFT can improve performance on specific classification tasks.
  • This study expands on existing work by evaluating PEFT's impact on classification performance and computational costs relative to full fine-tuning.

Purpose of the Study:

  • To investigate the influence of Adapters and LoRA on multilingual text classification performance and computational costs.
  • To analyze the efficacy of PEFT across various multilingual text classification tasks, including genre, framing, and persuasion detection.
  • To assess PEFT's performance under different training scenarios (original multilingual data, English translations, English-only subsets) and across diverse languages, especially for tasks with limited training data.

Main Methods:

  • Comparative analysis of parameter-efficient fine-tuning (PEFT) techniques (Adapters, LoRA) against full fine-tuning.
  • Evaluation on multilingual text classification tasks with varying input lengths, class numbers, and difficulty levels.
  • In-depth analysis of PEFT performance across different training data configurations and languages.

Main Results:

  • Parameter-efficient fine-tuning techniques demonstrate competitive performance and efficiency in multilingual text classification.
  • Findings highlight the applicability of PEFT for multilabel classification and non-parallel multilingual tasks.
  • Efficacy varies based on training data scenarios and specific languages, offering nuanced insights.

Conclusions:

  • Adapters and LoRA are viable and efficient alternatives to full fine-tuning for many multilingual text classification scenarios.
  • These techniques are particularly beneficial for tasks involving varied input lengths and limited data.
  • The study provides practical guidance on applying PEFT in diverse multilingual NLP research and applications.