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Deploying LLMs , GPT , and Hybrid AI
“ Future developments of LLMs will likely focus on enhancing interpretability and reducing biases to foster greater trust , ethical use and regulatory compliance ”
PRAMOD BELIGERE VICE PRESIDENT OF GENERATIVE AI PRACTICE HEAD , HEXAWARE
Increased complexity , potential exposure of proprietary information and security vulnerabilities all become a possibility .
Yet this does not need to be a situation of being inbetween a rock and a hard place , businesses can adopt a layered transparency approach .
“ Businesses can balance the pros of visibility with the cons of a more open security , providing sufficient detail to stakeholders without compromising proprietary information or security , and implementing robust governance frameworks and regularly auditing models to help manage risks while reaping the benefits ,” says Pramod .
Looking ahead , the landscape of LLMs is set to be one of greater inclusivity , transparency , and ethical use . Equally , regulations might further tighten to mandate more detailed documentation , bias audits and explainability requirements for AI models .
However it happens , and however the new challenges are balanced remains to be seen , yet what is becoming more clear is that the age of black box of LLMs may soon be coming to an end .
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