Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions

• URL: http://arxiv.org/abs/2211.11798v1
• Date: Mon, 21 Nov 2022 19:03:31 GMT
• Title: Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions
• Authors: Rafal Kocielnik, Sara Kangaslahti, Shrimai Prabhumoye, Meena Hari, R. Michael Alvarez, Anima Anandkumar
• Abstract summary: We propose a novel Active Transfer Few-shot Instructions (ATF) approach which requires no fine-tuning. ATF leverages the internal linguistic knowledge of pre-trained language models (PLMs) to facilitate the transfer of information. We show that annotation of just a few target-domain samples via active learning can be beneficial for transfer, but the impact diminishes with more annotation effort.
• Score: 58.69255121795761
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Labeling social-media data for custom dimensions of toxicity and social bias is challenging and labor-intensive. Existing transfer and active learning approaches meant to reduce annotation effort require fine-tuning, which suffers from over-fitting to noise and can cause domain shift with small sample sizes. In this work, we propose a novel Active Transfer Few-shot Instructions (ATF) approach which requires no fine-tuning. ATF leverages the internal linguistic knowledge of pre-trained language models (PLMs) to facilitate the transfer of information from existing pre-labeled datasets (source-domain task) with minimum labeling effort on unlabeled target data (target-domain task). Our strategy can yield positive transfer achieving a mean AUC gain of 10.5% compared to no transfer with a large 22b parameter PLM. We further show that annotation of just a few target-domain samples via active learning can be beneficial for transfer, but the impact diminishes with more annotation effort (26% drop in gain between 100 and 2000 annotated examples). Finally, we find that not all transfer scenarios yield a positive gain, which seems related to the PLMs initial performance on the target-domain task.

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