Everything You Should Know About Evaluating Large Language Models

<p>As open source language models become more readily available, getting lost in all the options is easy.</p> <p>How do we determine their performance and compare them? And how can we confidently say that one model is better than another?</p> <p>This article provides some answers by presenting training and evaluation metrics, and general and specific benchmarks to have a clear picture of your model&rsquo;s performance.</p> <p>If you missed it, take a look at the first article in the Open Language Models series:</p> <h2>&nbsp;</h2> <h1>Perplexity</h1> <p>Language models define a probability distribution over a vocabulary of words to select the most likely next word in a sequence. Given a text, a language model assigns a probability to each word in the language, and the most likely is selected.</p> <p><strong>Perplexity&nbsp;</strong>measures how well a language model can predict the next word in a given sequence. As a training metric, it shows how well the models learned its training set.</p> <p><a href="https://towardsdatascience.com/everything-you-should-know-about-evaluating-large-language-models-dce69ef8b2d2">Website</a></p>