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 Significantly Outperform Zero-Shot Generative AI Models in Text Classification&subtitle=Generative AI offers a simple%2C prompt-based alternative to fine-tuning smaller BERT-style LLMs for text classification tasks. This promises to eliminate the need for manually labeled training data and task-specific model training. However%2C it remains an open question whether tools like ChatGPT can deliver on this promise. In this paper%2C we show that smaller%2C fine-tuned LLMs (still) consistently and significantly outperform larger%2C zero-shot prompted models in text classification. We compare three major generative AI models (ChatGPT with GPT-3.5%2FGPT-4 and Claude Opus) with several fine-tuned LLMs across a diverse set of classification tasks (sentiment%2C approval%2Fdisapproval%2C emotions%2C party positions) and text categories (news%2C tweets%2C speeches). We find that fine-tuning with application-specific training data achieves superior performance in all cases. To make this approach more accessible to a broader audience%2C we provide an easy-to-use toolkit alongside this paper. Our toolkit%2C accompanied by non-technical step-by-step guidance%2C enables users to select and fine-tune BERT-like LLMs for any classification task with minimal technical and computational effort.?quality=80&w=800)
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