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Assessing the quality of information extraction
April 8, 2024, 4:46 a.m. | Filip Seitl, Tom\'a\v{s} Kov\'a\v{r}\'ik, Soheyla Mirshahi, Jan Kry\v{s}t\r{u}fek, Rastislav Dujava, Mat\'u\v{s} Ondrei\v{c}ka, Herbert Ullrich, Petr
cs.CL updates on arXiv.org arxiv.org
Abstract: Advances in large language models have notably enhanced the efficiency of information extraction from unstructured and semi-structured data sources. As these technologies become integral to various applications, establishing an objective measure for the quality of information extraction becomes imperative. However, the scarcity of labeled data presents significant challenges to this endeavor. In this paper, we introduce an automatic framework to assess the quality of the information extraction and its completeness. The framework focuses on information …
abstract advances applications arxiv become challenges cs.cl data data sources efficiency extraction however information information extraction integral language language models large language large language models quality structured data technologies type unstructured
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