In context Learning
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Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 25/100
- Issue type
- Documentation
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python
- Domain
- documentation
Research direction
Start with the Colab notebook referenced in the issue and trace how the question-answer pairs are supplied to the model. Done means documenting whether the behavior comes from those examples alone or from preliminary training or fine-tuning, with enough detail for readers to follow the process.
Written by the indexing model from the issue text.
Description
Can you explain in detail how the in context learning is done? Is it only by giving the question answer pair in the code as we see in colab notebook or is there a preliminary training or fine tuning of LLM done?
- Dominant language
- Python
- Stars
- 713
- Forks
- 127
- PR merge metrics
- No merged PRs in 30d
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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