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Open-Source LLMs in Financial Services
- Open-source LLMs provide a flexible and secure foundation for building AI-powered solutions for financial services.
- Financial institutions can embrace innovation while maintaining control over their data and algorithms.
- Data protection through strict adherence to data privacy laws and regulations is critical when deploying open-source LLMs.
- Bias and fairness, explainability, scalability, data privacy are the primary concerns with open-source LLMs.
- Transparency and accountability are critical for mitigating biases in open-source LLMs.
- Performance optimisation and support, continuous monitoring and maintenance, regulatory compliance, and integration into business processes add to the investment costs.
- Preventing the misuse of open-source LLMs is critical and requires robust input validation, exposure with adversarial examples during training, and controlled access.
- Enterprises need to engage in scalability conversations and figure out where they stand before adopting open-source LLMs.
- Customisation is a significant advantage with open-source LLMs and allows for tailoring solutions to meet specific financial regulations and requirements.
- Robust encryption protocols, data anonymisation, and comprehensive data governance policies are essential for protecting sensitive information.
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