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AI & MLRecommendedAIML-LM-028

Long-Context Modeling

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

Difficulty
4 / 5
Importance
4 / 5
Study time
1–2 h
Kind of work
Mixed

Why it matters

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

The route to this topic

Route 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