ManySpace
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Yapay ZekâÖnerilenAIML-EVAL-006

LLM Evaluation

Benchmarks, contamination, and reading leaderboard numbers honestly.

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 explain benchmark contamination and discount a leaderboard claim correctly.

Bu konuya giden rota

Rota AIML-EVAL-006
What Is a Model? Learning from Data Tasks: Regression, Classification, and Beyond Features and Targets Datasets and Splits Classification Framing Linear Regression I: Prediction and Residuals Tokens and Tokenization Train, Validation, and Test Errors Scores and Logits Loss and Fitting: Squared Error Offline Evaluation Multiclass Scores and Softmax Autoregression Objective Functions and Empirical Risk Next-Token Prediction Gradient Descent in Machine Learning Likelihood Generation Metrics Stochastic and Mini-batch Optimization Cross-Entropy Loss The Language-Modeling Objective Pretraining LLM Evaluation