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\section{Summary}\label{sec:summary} | ||
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We have demonstrated that LLMs can provide cost-effective, and high-quality annotations in the settign of multilingual, multilabel website topic classification. Our approach, which involved finetuning a pre-trained Homepage2vec model on LLM-generated labels, resulted in a improvement of 4.3 percentage points in the macro F1 score. Additionally, we are releasing the \texttt{curlie-gpt3.5-10k} and \texttt{curlie-gpt4-10k} datasets \cite{curlie-gpt-10k} with the intention of supporting further research in the open-source community. | ||
We have demonstrated that LLMs can provide cost-effective, and high-quality annotations in the settign of multilingual, multilabel website topic classification. Our approach, which involved finetuning a pre-trained Homepage2vec model on LLM-generated labels, resulted in a improvement of 4.3 percentage points in the macro F1 score. | ||
Additionally, the \texttt{curlie-gpt3.5-10k} and \texttt{curlie-gpt4-10k} datasets \cite{curlie-gpt-10k} are being released to aid open-source research. |