{"id":1510,"date":"2024-06-26T09:47:49","date_gmt":"2024-06-26T09:47:49","guid":{"rendered":"https:\/\/catedramasmovil.uc3m.es\/2024\/06\/26\/language-and-information-retrieval-models-for-enterprise-policy-question-management\/"},"modified":"2024-06-27T16:55:12","modified_gmt":"2024-06-27T16:55:12","slug":"language-and-information-retrieval-models-for-enterprise-policy-question-management","status":"publish","type":"post","link":"https:\/\/catedramasmovil.uc3m.es\/en\/2024\/06\/26\/language-and-information-retrieval-models-for-enterprise-policy-question-management\/","title":{"rendered":"Large Language Models and Information Retrieval for Enterprise Policy Question Management."},"content":{"rendered":"

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<\/h2>\n

Objective<\/h2>\n

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The project was born as a solution to a problem in managing HelpDesk inquiries for Human Resources in a telecommunications company. The resource consumption of the current service has been evaluated and compared with a solution developed using generative artificial intelligence (GenAI) in the form of a chatbot.<\/p>\n

The chatbot is designed to provide accurate responses to employees by consulting personal policies through a retrieval-augmented generation (RAG) system, which searches for information in relevant documents.<\/p>\n

For knowledge storage, both vector and relational databases were analyzed, with the relational database being chosen as the solution. This includes pre-processing of the text which is stored separately according to the index. Experiments have shown that this method provides better results than vector databases.<\/p>\n

Experiments have shown that this method provides better results than vector databases.<\/p>\n

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BACHELOR’S THESIS BY:<\/strong><\/span><\/p>\n

CARMEN ABELLA MIRAVET<\/strong><\/span><\/p>\n

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Academic Experience<\/strong><\/p>\n