Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach. (arXiv:2209.06995v1 [cs.CL])

We propose PATRON, a new method that uses prompt-based uncertainty estimation
for data selection 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%. …

arxiv data fine-tuning language language model model fine-tuning uncertainty