What Makes Pre-trained Language Models Better Zero-shot Learners?

• URL: http://arxiv.org/abs/2209.15206v3
• Date: Tue, 16 May 2023 02:45:51 GMT
• Title: What Makes Pre-trained Language Models Better Zero-shot Learners?
• Authors: Jinghui Lu, Dongsheng Zhu, Weidong Han, Rui Zhao, Brian Mac Namee, Fei Tan
• Abstract summary: Current methods for prompt learning in zeroshot scenarios rely on a development set with sufficient human-annotated data. We propose a simple yet effective method for screening reasonable prompt templates in zero-shot text classification: Perplexity Selection (Perplection) Experiments show that our method leads to improved prediction performance in a realistic zero-shot setting, eliminating the need for any labelled examples.
• Score: 12.164678440185007
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Current methods for prompt learning in zeroshot scenarios widely rely on a development set with sufficient human-annotated data to select the best-performing prompt template a posteriori. This is not ideal because in a realworld zero-shot scenario of practical relevance, no labelled data is available. Thus, we propose a simple yet effective method for screening reasonable prompt templates in zero-shot text classification: Perplexity Selection (Perplection). We hypothesize that language discrepancy can be used to measure the efficacy of prompt templates, and thereby develop a substantiated perplexity-based scheme allowing for forecasting the performance of prompt templates in advance. Experiments show that our method leads to improved prediction performance in a realistic zero-shot setting, eliminating the need for any labelled examples.

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