Cold-Start Data Selection for Better Few-shot Language Model Fine-tuning: A Prompt-based Uncertainty Propagation Approach

Yue Yu, Rongzhi Zhang, Ran Xu, Jieyu Zhang, Jiaming Shen, Chao Zhang

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Main: Machine Learning for NLP Main-poster Paper

Poster Session 3: Machine Learning for NLP (Poster)
Conference Room: Frontenac Ballroom and Queen's Quay
Conference Time: July 11, 09:00-10:30 (EDT) (America/Toronto)
Global Time: July 11, Poster Session 3 (13:00-14:30 UTC)
Keywords: few-shot learning, human-in-the-loop / active learning
TLDR: We present PATRON, a prompt-based data selection method for pre-trained language model fine-tuning under cold-start scenarios, i.e., no initial labeled data are available. In PATRON, we design (1) a prompt-based uncertainty propagation approach to estimate the importance of data points and (2) a par...
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Abstract: We present PATRON, a prompt-based data selection method for pre-trained language model fine-tuning under cold-start scenarios, i.e., no initial labeled data are available. In PATRON, we design (1) a prompt-based uncertainty propagation approach to estimate the importance of data points and (2) a partition-then-rewrite (PTR) strategy to promote sample diversity when querying for annotations. Experiments on six text classification datasets show that PATRON outperforms the strongest cold-start data selection baselines by up to 6.9\%. Besides, with 128 labels only, PATRON achieves 91.0\% and 92.1\% of the fully supervised performance based on vanilla fine-tuning and prompt-based learning respectively. Our implementation of PATRON will be published upon acceptance.