Practice How Are These Models Trained? - 2.3 | Understanding AI Language Models | Prompt Engineering fundamental course
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is data collection in the context of training language models?

๐Ÿ’ก Hint: Think of where the model gets its information.

Question 2

Easy

Define tokenization.

๐Ÿ’ก Hint: It's like cutting a sentence into smaller parts.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the purpose of tokenization in training language models?

  • To collect data
  • To divide text into smaller units
  • To evaluate the model

๐Ÿ’ก Hint: Think about the process of making data manageable.

Question 2

True or False: Fine-tuning helps align a model's responses with human expectations.

  • True
  • False

๐Ÿ’ก Hint: Consider how feedback can change oneโ€™s work.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a training pipeline for a new language model, including strategies for each step from data collection to RLHF.

๐Ÿ’ก Hint: Think about each step's contribution to the modelโ€™s capability.

Question 2

Analyze how insufficient data collection may impact the performance of a language model. What steps could be taken to mitigate this issue?

๐Ÿ’ก Hint: Consider the consequences of having a limited view versus a comprehensive one.

Challenge and get performance evaluation