A Hands-on Guide to LLM Arena-as-a-Judge
...with step-by-step implementation.
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A hands-on guide to LLM Arena-as-a-Judge
Most LLM evals assume you’re scoring one output at a time in isolation, without understanding the alternative.
So when prompt A scores 0.72 and prompt B scores 0.74, you still don’t know which one’s actually “better”.
This adds complexity, and since outputs are judged in isolation, direct comparisons between them are difficult.
LLM Arena-as-a-Judge is an idea that solves this!
In a gist…
Instead of assigning scores, you just run A vs. B comparisons and pick the better output.
You can define what “better” means (e.g., more helpful, more concise, more polite), and use GPT-4 (or any model) to act as the judge.
Let’s see how we can use LLM Arena-as-a-Judge directly in DeepEval (open-source) in just 10 lines of code.
After installing DeepEval (pip install deepeval), create an ArenaTestCase , with a list of “contestants” and their respective LLM interactions:
Next, define your criteria for comparison using the Arena G-Eval metric, which incorporates the G-Eval algorithm for a comparison use case:
Finally, run the evaluation and print the scores:
Done!
We got the head-to-head comparison we were looking for.
Note that LLM Arena-as-a-Judge can either be referenceless or reference-based. If needed, you can specify an expected output as well for the given input test case and specify that in the evaluation parameters.
One good thing about Arena G-Eval is that it uses the existing G-Eval algorithm for choosing the winning contestant.
Thus, one can simply define what’s a “better” output in everyday language and get the evaluation scores.
You can read about LLM Arena-as-a-Judge evals in the docs here →
Thanks for reading!
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