Examinando por Materia "Grandes modelos de lenguaje"

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    Evaluación de rendimiento de diferentes modelo grandes de lenguaje para el reconocimiento de emociones en texto
    (Universidad EAFIT, 2024) López Atehortúa, David Alejandro; Montoya Múnera, Edwin Nelson
    It is becoming more common for people to express their opinions in short texts through different media thanks to the expansion of internet access. Understanding and efficiently analyzing an individual’s sentiment from a text is a task that is useful in multiple scenarios. For the above, a branch of computer science called Natural Language Processing (NLP) has been dedicated to developing techniques to understand everything related to human language. Traditional techniques, based on the frequency of a word or a group of consecutive words to classify the text in a positive, negative or neutral sentiment. These techniques have limitations because they fail to capture the full context of each word in a sentence, affecting their accuracy and ability to detect a more detailed spectrum of emotions. Recently, Long Language Models (LLMs) or Transformers revolutionized the way NLP is performed thanks to their ability to capture the context around each word in a text. This allows for the detection of feelings in a more precise way and even, the classification of the text into a more specific emotion such as joy, optimism, anger, sadness or others. This project aims to evaluate the performance of different LLMs to find the best performing one in emotion detection from short texts in English using datasets typically used in research related to NLP models.
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    Respuestas a preguntas en contratos de arrendamiento bajo la normativa ASC (Accounting Standards Codification) 842 utilizando grandes modelos de lenguaje
    (Universidad EAFIT, 2025) Armendáriz Peña, David Adrián; Olarte Hernández, Tomás
    The ASC 842 standard, part of GAAP (Generally Accepted Accounting Principles) in the United States, establishes rules for recording leases in financial statements, enhancing transparency and comparability. However, its implementation poses significant challenges, such as interpreting complex contracts and extracting key information, tasks often performed manually, leading to high costs and errors. This thesis develops an automated system to address relevant questions about lease contracts using Natural Language Processing, Large Language Models, and Retrieval Augmented Generation. The goal is to reduce reliance on external consultants by identifying the information needed to draft technical accounting memos automatically. The GenAI Lifecycle methodology was employed, including text vectorization using embedding models and data storage in vector databases like Pinecone. Using lease contracts obtained from the Security Exchange Comission, the system was developed to answer key questions such as dates, purchase options, or renewal terms, achieving at least 70% accuracy. The results demonstrate that the system significantly reduces the time and costs associated with contract analysis, improving the accuracy in compliance with ASC 842. This approach has practical implications for the accounting industry, offering a scalable solution that democratizes access to advanced artificial intelligence tools, enabling companies to efficiently manage their regulatory processes. This work represents a significant step forward in integrating artificial intelligence to solve real-world accounting problems, fostering innovation in the extraction and analysis of regulatory information.