Pre-Trained Large Language Models
Explain how pre-trained transformer language models support generation and zero-shot task adaptation.
80 min
What you will learn
- Explain how pre-trained transformer language models support generation and zero-shot task adaptation.
- Design prompts for a target task and use perplexity and task evidence to assess the result.
Prerequisites
- Named Entity Recognition
Estimated time
80 minOptional diagnostic
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Pre-Trained Large Language Models
Pre-trained language models learn statistical patterns from broad text corpora with objectives such as next-token prediction. They can then be adapted through instructions, examples in context, or fine-tuning. GPT names a family of generative pre-trained transformers; success without weight updates depends on the task, context, and evaluation rather than being guaranteed for every query.
Text Generation and Perplexity
A causal language model estimates the next token conditioned on earlier tokens:
Practice
Labs and assignment
Key takeaways
- Explain how pre-trained transformer language models support generation and zero-shot task adaptation.
- Design prompts for a target task and use perplexity and task evidence to assess the result.
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