This blog represents the first in a series of investigations we’re running at Databricks to provide learnings on LLM evaluation.

Recently, the LLM community has been exploring the use of “LLMs as a judge” for automated evaluation with many using powerful LLMs such as GPT-4 to do the evaluation for their LLM outputs.

Using the Few Shots prompt with GPT-4 didn’t make an obvious difference in the consistency of results.

Including few examples for GPT-3.5-turbo-16k significantly improves the consistency of the scores, and makes the result usable.

...evaluation results can’t be transferred between use cases and we need to build use-case-specific benchmarks in order to properly evaluate how good a model can meet customer needs.

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