An AI team is building a general-purpose chatbot. They train two different models on a large dataset of text summarization tasks.
- Model A is trained using 100,000 different articles, but every training example uses the exact same instruction: "Summarize the following text."
- Model B is trained using only 10,000 different articles, but the training examples use 1,000 varied instructions for summarization (e.g., "Give me the gist," "What are the key points?," "Provide a brief overview.").
When a user gives the prompt, "Can you give me the TL;DR for this article?", which model is more likely to fail at the task, and what is the most probable reason for its failure?
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
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Analysis in Bloom's Taxonomy
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An AI team is building a general-purpose chatbot. They train two different models on a large dataset of text summarization tasks.
- Model A is trained using 100,000 different articles, but every training example uses the exact same instruction: "Summarize the following text."
- Model B is trained using only 10,000 different articles, but the training examples use 1,000 varied instructions for summarization (e.g., "Give me the gist," "What are the key points?," "Provide a brief overview.").
When a user gives the prompt, "Can you give me the TL;DR for this article?", which model is more likely to fail at the task, and what is the most probable reason for its failure?
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