---
title: "Model Evaluation"
type: concept
related: [Perplexity, Loo]
source: https://www.jemoka.com/posts/kbhmodel_evaluation/
confidence: high
status: active
---

Some ideas of model validation
Cross Validation Hold-out cross-validation For instance, you can do:
70% for training 30% hold out cross validation for testing But at very large dataset scales, the validation size can be capped at a fixed size (so you can hold out like 0.1% or something but still have 10k samples).
k-fold cross validation shuffle the data divide the data into \(k\) equal sized pieces repeatedly train the algorithm on 4/5 of the data, test on remaining 1/5 In practice people do 10 folds.
LOOCV See Leave-One-Out Cross Validation
Test Set For academic settings, not for production, we can report the result on a third unbiased estimate of the dataset.
LLM Evaluation Types Intrinsic Evaluation In-Vitro Evaluation or Intrinsic Evaluation focuses on evaluating the language models&rsquo; performance at, well, language modeling.
Typically, we use perplexity.
directly measure language model performance doesn&rsquo;t necessarily correspond with real applications Extrinsic Evaluation Extrinsic Evaluation, also known as In-Vivo Evaluation, focuses on benchmarking two language models in terms of their differing performance on a test task.
