Benchmarking Llama2, Mistral, Gemma and GPT for Factuality, Toxicity,
  Bias and Propensity for Hallucinations
David Nadeau Mike Kroutikov Karen McNeil Simon Baribeau
This paper introduces fourteen novel datasets for the evaluation of Large
Language Models' safety in the context of enterprise tasks. A method was
devised to evaluate a model's safety, as determined by its ability to follow
instructions and output factual, unbiased, grounded, and appropriate content.
In this research, we used OpenAI GPT as point of comparison since it excels at
all levels of safety. On the open-source side, for smaller models, Meta Llama2
performs well at factuality and toxicity but has the highest propensity for
hallucination. Mistral hallucinates the least but cannot handle toxicity well.
It performs well in a dataset mixing several tasks and safety vectors in a
narrow vertical domain. Gemma, the newly introduced open-source model based on
Google Gemini, is generally balanced but trailing behind. When engaging in
back-and-forth conversation (multi-turn prompts), we find that the safety of
open-source models degrades significantly. Aside from OpenAI's GPT, Mistral is
the only model that still performed well in multi-turn tests.