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pro vyhledávání: '"Stogiannidis, Ilias"'
Autor:
Loukas, Lefteris, Stogiannidis, Ilias, Diamantopoulos, Odysseas, Malakasiotis, Prodromos, Vassos, Stavros
Standard Full-Data classifiers in NLP demand thousands of labeled examples, which is impractical in data-limited domains. Few-shot methods offer an alternative, utilizing contrastive learning techniques that can be effective with as little as 20 exam
Externí odkaz:
http://arxiv.org/abs/2311.06102
Prompting Large Language Models (LLMs) performs impressively in zero- and few-shot settings. Hence, small and medium-sized enterprises (SMEs) that cannot afford the cost of creating large task-specific training datasets, but also the cost of pretrain
Externí odkaz:
http://arxiv.org/abs/2310.13395
We propose the use of conversational GPT models for easy and quick few-shot text classification in the financial domain using the Banking77 dataset. Our approach involves in-context learning with GPT-3.5 and GPT-4, which minimizes the technical exper
Externí odkaz:
http://arxiv.org/abs/2308.14634