In-Context Learning
A method in which the model learns to perform a task from the context supplied in the request. The approach lets the model adapt to new tasks without any special training, using only the information provided in the prompt.
| Advantages | Drawbacks |
|---|---|
| High flexibility and adaptability across different tasks | Task quality depends on the context you provide |
| Requires no additional model training | Can be less effective for very complex tasks |
| Lets you solve new tasks "on the fly" | Limited by how much context fits into the prompt |
In-Context Learning example:
Consider the following examples of emotion classification in text:
1. 'I'm so glad I passed the exam!' - Joy
2. 'I'm really sad that my friend is leaving.' - Sadness
3. 'I'm furious about this injustice!' - AngerNow classify the emotion in the following sentence: 'I'm so nervous before an important presentation.'