ManySpace
English Giriş yap Kayıt ol

Yapay ZekâÖnerilenAIML-LM-028

Long-Context Modeling

Extending context windows through position interpolation and efficient attention, and the lost-in-the-middle problem.

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

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

Neden önemli

The learner can explain how position interpolation extends a trained context window and interpret a needle-in-a-haystack accuracy curve.

Bu konuya giden rota

Rota AIML-LM-028
What Is a Model? Tasks: Regression, Classification, and Beyond Features and Targets Classification Framing Sequences and Order Linear Regression I: Prediction and Residuals Embeddings Tokens and Tokenization Scores and Logits Loss and Fitting: Squared Error Similarity in Vector Space Token Representation Multiclass Scores and Softmax The Artificial Neuron Autoregression Query, Key, Value Position Information Activation Functions Similarity Scores Next-Token Prediction Positional Encoding Schemes and RoPE The Multilayer Perceptron Weighted Aggregation Self-Attention The Attention Block Context and Conditioning The Causal Transformer Context Windows Long-Context Modeling