Answer / Mr Sumant Kumar

General-purpose Language Models (LLMs) are designed to perform a wide range of natural language processing tasks, such as text generation, translation, summarization, etc., whereas task-specific and domain-specific LLMs are tailored for specific applications or domains. Task-specific models focus on solving a particular task, like machine translation or question answering, while domain-specific models specialize in understanding the characteristics of a specific domain, such as medical or legal text.

How is Generative AI used in healthcare?

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How do you communicate complex AI concepts to non-technical stakeholders?

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What strategies can be used to adapt LLMs to a specific use case?

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How do you approach working with incomplete or ambiguous requirements?

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What is Generative AI, and how does it differ from traditional AI models?

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How do you identify and mitigate bias in Generative AI models?

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How does transfer learning play a role in training LLMs?

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Can you explain the historical context of Generative AI and how it has evolved?

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What factors should be considered when comparing small and large language models?

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How do you select the right model architecture for a specific Generative AI application?

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What measures do you take to secure sensitive data during model training?

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How do foundation models support Generative AI systems?

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