Knowledge Check

The following questions will help you check what you've learned.

1. Question 1 of 3. Select three.Which methods can be used for fine-tuning a foundation model (FM)? (Choose THREE.)

   AInstruction tuningBROUGECTransfer learningDReinforcement learning from human feedback (RLHF)EModel pruningFBLEU
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2. Question 2 of 3You are part of a team working on fine-tuning a large language model for a specific domain. To ensure the model's performance is optimized for the target domain, you need to carefully prepare the dataset. Which of the following steps is most critical in the fine-tuning data preparation process to ensure the model's specialization and accuracy in the target domain?

   ALabeling with accurate and relevant labelsBEnsuring data governance and compliance with industry regulationsCChecking for representativeness and addressing potential biasesDIncorporating user or expert feedback
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3. Question 3 of 3You are evaluating the performance of a language generation model on various text generation tasks, such as machine translation, summarization, and open-ended text generation. To assess the quality of the generated text, you need to choose an appropriate evaluation metric that can capture the semantic similarity between the model's output and human-generated reference texts. Based on the information provided, which evaluation metric would be the most suitable for this scenario?

   AROUGEBBLEUCBERTScoreDPerplexity
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