March 28, 2024, 4:48 a.m. | Toyin Aguda, Suchetha Siddagangappa, Elena Kochkina, Simerjot Kaur, Dongsheng Wang, Charese Smiley, Sameena Shah

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arXiv:2403.18152v1 Announce Type: new
Abstract: Collecting labeled datasets in finance is challenging due to scarcity of domain experts and higher cost of employing them. While Large Language Models (LLMs) have demonstrated remarkable performance in data annotation tasks on general domain datasets, their effectiveness on domain specific datasets remains underexplored. To address this gap, we investigate the potential of LLMs as efficient data annotators for extracting relations in financial documents. We compare the annotations produced by three LLMs (GPT-4, PaLM 2, …

abstract annotation arxiv cost data data annotation datasets domain domain experts efficiency experts finance financial general language language models large language large language models llms performance study tasks them type

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