Entailment as Robust Self-Learner

• URL: http://arxiv.org/abs/2305.17197v1
• Date: Fri, 26 May 2023 18:41:23 GMT
• Title: Entailment as Robust Self-Learner
• Authors: Jiaxin Ge, Hongyin Luo, Yoon Kim, James Glass
• Abstract summary: We design a prompting strategy that formulates a number of different NLU tasks as contextual entailment. We propose the Simple Pseudo-Label Editing (SimPLE) algorithm for better pseudo-labeling quality in self-training.
• Score: 14.86757876218415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Entailment has been recognized as an important metric for evaluating natural language understanding (NLU) models, and recent studies have found that entailment pretraining benefits weakly supervised fine-tuning. In this work, we design a prompting strategy that formulates a number of different NLU tasks as contextual entailment. This approach improves the zero-shot adaptation of pretrained entailment models. Secondly, we notice that self-training entailment-based models with unlabeled data can significantly improve the adaptation performance on downstream tasks. To achieve more stable improvement, we propose the Simple Pseudo-Label Editing (SimPLE) algorithm for better pseudo-labeling quality in self-training. We also found that both pretrained entailment-based models and the self-trained models are robust against adversarial evaluation data. Experiments on binary and multi-class classification tasks show that SimPLE leads to more robust self-training results, indicating that the self-trained entailment models are more efficient and trustworthy than large language models on language understanding tasks.

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