Low-resource Interactive Active Labeling for Fine-tuning Language Models
Seiji Maekawa, Dan Zhang, Hannah Kim, Sajjadur Rahman, Estevam Hruschka
Abstract
Recently, active learning (AL) methods have been used to effectively fine-tune pre-trained language models for various NLP tasks such as sentiment analysis and document classification. However, given the task of fine-tuning language models, understanding the impact of different aspects on AL methods such as labeling cost, sample acquisition latency, and the diversity of the datasets necessitates a deeper investigation. This paper examines the performance of existing AL methods within a low-resource, interactive labeling setting. We observe that existing methods often underperform in such a setting while exhibiting higher latency and a lack of generalizability. To overcome these challenges, we propose a novel active learning method TYROUGE that employs a hybrid sampling strategy to minimize labeling cost and acquisition latency while providing a framework for adapting to dataset diversity via user guidance. Through our experiments, we observe that compared to SOTA methods, TYROUGE reduces the labeling cost by up to 43% and the acquisition latency by as much as 11X, while achieving comparable accuracy. Finally, we discuss the strengths and weaknesses of TYROUGE by exploring the impact of dataset characteristics.- Anthology ID:
- 2022.findings-emnlp.235
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3230–3242
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.235
- DOI:
- 10.18653/v1/2022.findings-emnlp.235
- Cite (ACL):
- Seiji Maekawa, Dan Zhang, Hannah Kim, Sajjadur Rahman, and Estevam Hruschka. 2022. Low-resource Interactive Active Labeling for Fine-tuning Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3230–3242, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Low-resource Interactive Active Labeling for Fine-tuning Language Models (Maekawa et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.findings-emnlp.235.pdf