How do you assess whether a language model works properly?

A frequently asked question that we receive is: how do you actually assess whether a language model works properly? Before we answer that question in this article; let's take a look at how we assess other statistical models.

1. Evaluation of explanatory statistical models:

Let's start with explanatory statistical models, such as regression analyses. In a regression analysis, we investigate the causality between different variables. By plotting all observations on a graph and drawing a linear line, we try to see if there is a linear relationship between the variables. The R-square, also known as the proportion of explained variance, is used to evaluate the quality of the model. The higher the R-square, the better the model predicts and explains the phenomenon.

2. Assessment of predictive statistical models:

In predictive statistical models, such as Machine Learning models, we evaluate whether the model works properly by means of a training set and a test set. We develop a model that makes predictions and compare them with reality. The higher the percentage of predicted values that match the observed values, the better the model performs.

3. Evaluate the quality of language models:

But how do we assess the quality of language models? This differs significantly from other statistical models. The essence lies in practice. A language model is pre-trained and evaluated, but it's all about how well it can adapt to company-specific information and provide consistently relevant answers. There are no specific statistical measures for assessing the quality of the model itself, because it ultimately depends on how well it performs in real-life case studies.

Conclusion: Unlike traditional statistical models, language models require a practical approach to evaluation. It's not about finding statistical measures to measure the quality of the model itself, but rather about seeing how effective the model is in real situations. You might say 'The Proof of the Pudding is in the Eating' - the proof of the pudding is in the food; you need to test the model extensively, fine-tune it and apply it to practical cases to discover its true value.

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