178 citations · 455 across the 19 of their papers we have counts for
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Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking
Lefteris Loukas, Ilias Stogiannidis, Odysseas Diamantopoulos +2
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 contrasti…
Cache me if you Can: an Online Cost-aware Teacher-Student framework to Reduce the Calls to Large Language Models
Ilias Stogiannidis, Stavros Vassos, Prodromos Malakasiotis +1
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 creatin…
Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance
Lefteris Loukas, Ilias Stogiannidis, Prodromos Malakasiotis +1
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-contex…