ManySpace
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Yapay ZekâÖnerilenAIML-LM-022

Prompting and In-Context Learning

Steering a pretrained model with instructions and examples in the context window, without weight updates.

Bu konunun henüz Türkçe çevirisi yok, bu yüzden İngilizce gösteriliyor.

Zorluk
3 / 5
Önem
4 / 5
Çalışma süresi
1–2 sa
Çalışma türü
Karma

Neden önemli

The learner can vary the number and order of in-context examples and predict their effect on a model's answer.

Bu konuya giden rota

Rota AIML-LM-022
What Is a Model? Tasks: Regression, Classification, and Beyond Features and Targets Classification Framing Linear Regression I: Prediction and Residuals Embeddings Tokens and Tokenization Scores and Logits Loss and Fitting: Squared Error Similarity in Vector Space Multiclass Scores and Softmax Autoregression Query, Key, Value Similarity Scores Next-Token Prediction Weighted Aggregation Self-Attention Context and Conditioning Prompting and In-Context Learning