Don't rely too much on automated benchmarks for LLMs. They are often gamed, made to overfit, and result in worse performance in the general case.
Human evaluation is the gold standard and the Llama 2 paper gave significant evidence that Llama 2 70b chat is on-par, if not, better than ChatGPT for that metric so I tend to stick to it unless there is good reason not to.
The problem with Llama 2 chat versions is that they have been RLHF-ed to death. You can't ask questions without getting a sermon of how your question may be inappropriate for this or that reason.
I think it's worse on the smaller models, but still present in the 70B one.
Apologies if you’d already seen this and were only trying to make a point, but you might like this article from a week or 2 ago that talks about how to run Llama 2 “uncensored” locally, and it seems to do a decent job of mitigating the sermons!
When you encounter "uncensored" in a llama model (1 or 2) what that means in that context is that the fine-tuning datasets used have had all refusals to respond removed. There's no way to uncensor the pre-trained model itself and fine-tuning only changes the style of the output.
For sure, that's a good reason for using the uncensored fine-tuned versions. There are other good reasons too like expanded context size, codegen, and story writing/rp. Just be careful of extraordinary benchmarks.
Btw, have you tried changing the default Llama 2 chat prompt? Meta tried to fine-tune it so that if you remove the safety part from the prompt, safety won't be applied[1]. Not sure how well it works myself, but worth a shot I guess
Human evaluation is the gold standard and the Llama 2 paper gave significant evidence that Llama 2 70b chat is on-par, if not, better than ChatGPT for that metric so I tend to stick to it unless there is good reason not to.