This has been my experience as well with ChatGPT. Sure you can tell it to write like some other random persona or something but realistically it's always felt pretty obvious that something was written by ChatGPT. The more I interact with it the less excited I am about its writing capabilities because they always feel like they're written by spam blogs or something.
It wouldn't surprise me if the vast bulk of what they trained on from the Common Crawl was algorithmically written spam content anyhow. Not to mention at this point how potential datasets going forward are all going to be polluted with ai generated content, entrenching the bias in these training sets. I wouldn't be surprised if a certain percentage of HN comments now are from people testing language models. Certainly reddit and other popular websites have been polluted for years now even before the latest crop of gpts.
It’s hardly surprising when you consider that what gives a writer their distinct voice is to a large extent determined by their own particular diet of others’ writing, which in the case of ChatGPT is… well… everything. So of course you get blandness.
Certainly looks interesting. But why would you want to imitate other writers’ styles, except for pure novelty’s sake? You could also train an AI to imitate yourself, given enough content, but why would you? I’m not sure I fully understand the motivation.
Non-AI writers, including professionals, imitate other writers' styles all the time, whether that's specific writers or a general genre. For example, the Dresden Files series started out as an intentional homage/parody of potboiled detective works except with all the urban magic stuff added in, and retained much of that style over time, like the intentionally overdramatic internal narration.
Right, you have to practically rewrite the entire response in order to make it sound like something a human would write. Then you wonder if it was even worth the effort. It is decent at pulling up research notes, which you then have to thoroughly vet to make sure they are accurate before you use them.
It's the difference between the pretrained and chat/instruct fine tuned models.
The TextCompletion DaVinci was way better than the ChatCompletion model in variety of language.
Trying to get the chat model to generate marketing copy was laughable. It looked like a high school senior's idea of copywriting, and was nearly impossible to correct.
The base model was pretty easy to get great results from as long as effectively biasing the context towards professional copy.
Even the fact that you can't set the system messages at a core level is silly.
I can't have the model actually be told it is an award winning copywriter. Instead it effectively gets told "you are a LLM by OpenAI pretending to be an award winning copywriter."
Really too bad that 99% of the training data it was built on top of wasn't being written by a LLM by OpenAI.
It's effectively narrowing the context space unnecessarily and creating a bottleneck that severely limits the application of the SotA model, but it still scores well on the handful of tests it is being evaluated on so no one bats an eye as it seems no one at OpenAI has heard of Goodhart's Law.
