Evaluation of language-specific LLM accuracy on the global Massive Multitask Language Understanding benchmark in Python
—
As soon as a new LLM is released, the obvious question we ask ourselves is this: Is this LLM better than the one I’m currently using?
LLMs are typically evaluated against a large number of benchmarks, most of which are in English only.
For multilingual models, it is very rare to find evaluation metrics for every specific language that was in the training data.
Sometimes evaluation metrics are published for the base model and not for the model tuned to the instructions. And usually the evaluation is not done on the quantization model that we actually use locally.
So it is very unlikely to find comparable evaluation results from several LLMs in a specific language other than English.
Therefore, in this article, we will use the Global-MMLU dataset to perform our own evaluation using the widely used MMLU benchmark in the language of our choice.
Table Of Contents
· The Massive Multitask Language Understanding Benchmark
∘ MMLU
∘ Global-MMLU
· Deploying a Local LLM With vLLM
·…