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              <text>LegalMind System and the LLM-based Legal Judgment Query System</text>
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              <text>Artificial intelligence; Claude AI; Comparative analysis; Evaluation; Finance sector; Financial legal contexts; FLAN-T5-Base; Large Language Models (LLMs); Legal analysis; Legal documents; Legal insights; Legal Judgment Query System; LegalMind-GPT; LLAMA-2; Natural language processing; Research paper; Technology integration</text>
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              <text>LegalMind-GPT represents a notable advancement in legal technology, specifically tailored for the finance sector. This research paper introduces LegalMind-GPT, a system that integrates Large Language Models (LLMs) to develop a Legal Judgment Query System for financial legal contexts. The study focuses on the application of LLMs, particularly LLAMA-2, Claude AI, and FLAN-T5-Base, for interpreting and analysing complex legal documents in finance. The aim is to evaluate the system's effectiveness in providing accurate legal judgments and insights. The comparative analysis of these LLMs shows that LegalMind-GPT, powered by these models, significantly improves the accuracy and efficiency of legal analysis in the finance domain.   2024 IEEE.</text>
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              <text>Athul S.; Saxena A.; Mahajan J.; Panikulangara L.; Kulkarni S.; Bang D.S.</text>
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              <text>TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>&lt;a href="https://doi.org/10.1109/TQCEBT59414.2024.10545179" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/TQCEBT59414.2024.10545179&lt;/a&gt;
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              <text>ISBN: 979-835038427-7</text>
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              <text>Athul S., Christ University, India; Saxena A., Christ University, India; Mahajan J., Christ University, India; Panikulangara L., Synechron, India; Kulkarni S., Ibm, India; Bang D.S., Christ University, India</text>
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