Answer / Pooja Meena

"Hallucination in Language Models (LLMs) refers to the phenomenon where the model generates responses that are plausible but not supported by the input. This can occur when the model extrapolates beyond its training data or makes incorrect assumptions about the context. Techniques for controlling hallucination include fine-tuning the model on a specific task, using adversarial examples to train the model to be more robust against misleading inputs, and implementing mechanisms for detecting and correcting erroneous outputs."n

Can you explain the difference between discriminative and generative models?

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

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How can organizations create a culture of collaboration around Generative AI projects?

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

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Can you explain the key technologies and principles behind LLMs?

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What are the advantages of combining retrieval-based and generative models?

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How do you balance transparency and performance in Generative AI systems?

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

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How do you enforce data governance in Generative AI projects?

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How do you ensure that your LLM generates contextually accurate and meaningful outputs?

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How can one select the right LLM for a specific project?

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What role will Generative AI play in autonomous systems?

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