Knowledge Extraction Using LLMs

Author(s): Ori Cohen

Originally published on Towards AI.

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Knowledge extraction from documents using LLMs (Large Language Models) has become increasingly important in our data-driven world. As the volume of information grows exponentially, there’s a need to efficiently process and understand content from various sources, including text, tables, and figures.

LLMs represent a significant leap forward in knowledge extraction, offering substantial time and cost savings compared to traditional NLP (Natural Language Processing) methods. While conventional approaches often require extensive feature engineering and domain-specific models, LLMs can be applied to diverse tasks with minimal prompting. This versatility drastically reduces development time and the need for specialized expertise across different domains.

LLMs’ ability to understand context and nuance also means they can extract more accurate and relevant information, reducing the time spent on manual verification and error correction. Furthermore, their capacity to process and analyze vast amounts of data in parallel far outpaces human capabilities, allowing researchers to cover more ground in less time. This acceleration of the research process not only saves direct labor costs but also enables faster innovation and decision-making, providing organizations with a competitive edge.

Additionally, as LLMs improve in efficiency and become more… Read the full blog for free on Medium.

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Published via Towards AI