Arize Community Paper Reading: Exploring LibreEval
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About Event
Over the past few weeks, the Arize team has generated the largest public dataset of hallucinations, as well as a series of fine-tuned evaluation models.
We wanted to create a replicable, evolving dataset that can keep pace with model training so that you always know you're testing with data your model has never seen before. We also saw the prohibitively high cost of running LLM evals at scale, and have used our data to fine-tune a series of SLMs that perform just as well as their base LLM counterparts, but at 1/10 the cost.
Join us to hear about what we built, the process we took, and the bottom line results.