Answer / Mitan Verma

Masking works in Transformer models by randomly hiding some of the input tokens and training the model to predict their values. This technique is used for tasks such as language modeling, where the goal is to generate the next word given a sequence of words. Masking helps the model learn to focus on important information and ignore irrelevant details.

How do you prepare and clean data for training a generative model?

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What steps are involved in defining the use case and scope of an LLM project?

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How do few-shot and zero-shot learning influence prompt engineering?

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Why is it essential to observe copyright laws in LLM applications?

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What is the importance of attention mechanisms in LLMs?

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What are the challenges of using large datasets in LLM training?

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What are prompt engineering techniques, and how can they improve LLM outputs?

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What are some techniques to improve LLM performance for specific use cases?

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How do you integrate Generative AI with rule-based systems?

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