Large Language Model Evaluation Metrics

Author(s): Derrick Mwiti

Originally published on Towards AI.

This member-only story is on us. Upgrade to access all of Medium.

Photo by Possessed Photography on Unsplash

The most common evaluating metrics for large language models are:

PerplexityBLEUROUGEBERTScoreCOMETMETEORBLEURTGPTScorePRISMBARTScoreG-EvalHuman Evaluation

Evaluating large language models(LLMs) is extremely difficult due to the fact that they can perform a myriad of tasks.

Perplexity measures how good a model is at predicting the next word. The lower the score the better, hence a higher score means the model is performing poorly at coming up with the next word. Therefore, the objective is to minimize the language model’s perplexity. The English synonym for perplex is baffle or confuse. Hence, a model that’s good at predicting the next token is not baffled or confused.

The perplexity metric is better suited for auto-regressive models that generate text than masked language models such as BERT used for classification. The metric is computed as the exponentiated average exponential log-likelihood of a sequence. Since perplexity measures how well the model predicts the next token, it goes without saying that the tokenization process also affects the model’s perplexity.

Source

The perplexity of a large language model is closely related to cross-entropy. The cross-entropy loss is a common loss function in classification problems. The task of predicting the next word… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI