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Table Extraction Using LLMs
Nanonets evaluates multiple LLM APIs for table extraction, comparing their performance and summarizing the challenges, advantages, and drawbacks of each model.
There are merged cells, hierarchy of columns and rows, variation in fonts, and mixed data types across columnsThese factors create unique layouts that resist standardized parsing, necessitating more flexible, context-aware extraction methods. These transformer based deep neural networks, trained on vast amounts of data, can perform a wide range of natural language processing (NLP) tasks, such as translation, summarization, and sentiment analysis. Recent developments have expanded LLMs beyond text, enabling them to process diverse data types including images, audio, and video, thus achieving multimodal capabilities that mimic human-like perception.
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