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- W4385570908 abstract "Zero-shot prompt-based learning has made much progress in sentiment analysis, and considerable effort has been dedicated to designing high-performing prompt templates. However, two problems exist; First, large language models are often biased to their pre-training data, leading to poor performance in prompt templates that models have rarely seen. Second, in order to adapt to different domains, re-designing prompt templates is usually required, which is time-consuming and inefficient. To remedy both shortcomings, we propose a simple yet strong data construction method to de-bias a given prompt template, yielding a large performance improvement in sentiment analysis tasks across different domains, pre-trained language models, and prompt templates. Also, we demonstrate the advantage of using domain-agnostic generic responses over the in-domain ground-truth data." @default.
- W4385570908 created "2023-08-05" @default.
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- W4385570908 date "2023-01-01" @default.
- W4385570908 modified "2023-09-24" @default.
- W4385570908 title "A Simple Yet Strong Domain-Agnostic De-bias Method for Zero-Shot Sentiment Classification" @default.
- W4385570908 doi "https://doi.org/10.18653/v1/2023.findings-acl.242" @default.
- W4385570908 hasPublicationYear "2023" @default.
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