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      "text": "In the realm of financial analysis, extracting more than 40 financial variables from quarterly reports is an intricate endeavour, which is further magnified when deploying LLMs- which are typically attuned to unstructured text-to analyze structured table data found in financial documents. These reports feature tables filled with data organized in rows and columns, representing a unique challenge for LLMs that are adept at navigating textual data but less so with the intricate relationships and numerical nuances of tables. The extraction of financial variables from these reports is a more intricate task than that of ESG reporting, as it typically involves a greater number of variables. This represents a significant increase in data complexity when compared to ESG data extraction, making it a particularly challenging endeavor due to the sheer volume and intricacy of the financial information presented.\n\nThe EY approach to these challenges employs powerful search capabilities along with advanced table summarization techniques. This includes employing chain-of-thought and chain-of-verification processes to enhance the accuracy of the extracted data. Drawing on the experiences from ESG reporting, we developed a hybrid retrieval system that combines vector search with the BM25 algorithm, significantly increasing the reliability and precision of the data extraction process.",
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