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A company is deploying a large language model for a customer service chatbot. The model needs to understand industry-specific jargon and maintain low latency. Which approach best balances these requirements?
Fine-tuning a small open-source LLM on domain-specific data is the best approach because it adapts the model to understand industry-specific jargon while keeping the model small enough to maintain low latency. Unlike larger models, a fine-tuned small model can run efficiently on …Read full explanation
A data scientist observes that their fine-tuned LLM performs well on training data but generates repetitive and dull responses in production. What is the most likely cause and best solution?
The model's repetitive and dull responses indicate that the temperature parameter is too low, causing the model to always select the most probable tokens, leading to deterministic and monotonous outputs. Increasing temperature during inference introduces randomness into token sam…Read full explanation
An organization wants to use an LLM to summarize legal documents. Which consideration is most important for ensuring accurate summaries?
Legal documents require precise understanding, so fine-tuning on legal data is critical. Option B is wrong because larger models don't guarantee domain accuracy. Option C is wrong because pre-training from scratch is expensive and unnecessary. Option D is wrong because zero-shot …Read full explanation
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