LLM as a Judge: Evaluate Your LLMs with Another LLM
A good evaluation framework for quick feedback and monitoring
Evaluating large language models (LLMs) can be tricky. These models can do so many things, so it’s hard to come up with clear, simple standards to judge their responses. For example, an answer from an LLM might lack context, repeat itself, have grammar mistakes, be way too long, or even make little sense.
One effective solution is to let LLMs evaluate each other, an approach known as "LLM-as-a-judge." This approach which is used in popular benchmarks like Chatbot Arena, involves using an LLM to score or rank the responses of other models. By letting LLMs handle the judging, we can save on human effort while still getting feedback. As it's automatic, this method makes it easier to review and improve these models without relying heavily on human reviewers. LLM-as-a-judge is also a good alternative to avoid depending on old public benchmarks for evaluation like MMLU which have probably been seen by the models during training.
In this article, we will see how to use the LLM-as-a-judge framework, with examples. Using vLLM and TRL, we will see how to compare two LLMs using a third larger and better LLM as a judge and compute win rates.
I also wrote a notebook showing how to run LLM-as-a-judge, here: