Practice How Are These Models Trained? (2.3) - Understanding AI Language Models

Practice: How Are These Models Trained?

Lesson 2.3Understanding AI Language Models

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9 questions

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5 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is data collection in the context of training language models?
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Think of where the model gets its information.
Question 2.
Define tokenization.
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It's like cutting a sentence into smaller parts.
3Question 3.
What is the purpose of tokenization in training language models?
- To collect data
- To divide text into smaller units
- To evaluate the model
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Think about the process of making data manageable.
4Question 4.
True or False: Fine-tuning helps align a model's responses with human expectations.
- True
- False
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Consider how feedback can change one’s work.
Question 5.
Design a training pipeline for a new language model, including strategies for each step from data collection to RLHF.
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Think about each step's contribution to the model’s capability.
Question 6.
Analyze how insufficient data collection may impact the performance of a language model. What steps could be taken to mitigate this issue?
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Consider the consequences of having a limited view versus a comprehensive one.

5 more questions in this section. Enroll free to practise them all.