main webpage W Topic Machine Learning What Makes Pre-trained Language Models Better Zero-shot Learners? 2023 Current methods for prompt learning in zero-shot 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 real-world zero-shot scenario of practi… Article Open Machine Learning Open Article Page

Machine Learning

Study of algorithms that improve automatically through experience Machine learning ( ML ) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalise to unseen data, and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics. Critical Symbolic Virtual Narrative Open Machine Learning Open Article Page

Exploring foci of: What Makes Pre-trained Language Models Better Zero-shot Learners? 2023 Current methods for prompt learning in zero-shot 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 real-world 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… Open Article Page

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Explore Machine Learning W Topic Machine Learning What Makes Pre-trained Language Models Better Zero-shot Learners? 2023 Current methods for prompt learning in zero-shot 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 real-world zero-shot scenario of practi… Article Critical Symbolic Virtual Narrative Open Machine Learning Open Article Page