clownfish VS ChatGPT_DAN

I'm not sure how this is different than:

https://github.com/1rgs/jsonformer

or

https://github.com/newhouseb/clownfish

or

https://github.com/mkuchnik/relm

or

https://github.com/ggerganov/llama.cpp/pull/1773

or

https://github.com/Shopify/torch-grammar

Overall there are a ton of these logit based guidance systems, the reason they don't get tons of traction is the SOTA models are behind REST APIs that don't enable this fine-grained approach.

Those models perform so much better that people generally settle for just re-requesting until they get the correct format (and with GPT-4 that ends up being a fairly rare occurrence in my experience)

I can see some specific problems there, like malformed json (or json not matching intended schema being generated). Approaches like https://github.com/1rgs/jsonformer and https://github.com/newhouseb/clownfish could be interesting there, as well as approaches to validate outputs like https://medium.com/@markherhold/validating-json-patch-requests-44ca5981a7fc (references jsonpatch which could be interesting as well, but the approach is somewhat agnostic to how the changes actually get applied while still allowing you to enforce structure around what changes and how).

https://github.com/newhouseb/clownfish

Structural Alignment: Modifying Transformers (like GPT) to Follow a JSON Schema

Oh nice! I built a similar system a few weeks ago: https://github.com/newhouseb/clownfish

I think the main differentiating factor here is that this is better if you have a simpler JSON schema without enums or oneOf constraints. If you do have these constraints, i.e. let's say you wanted an array of different types that represented a items on a menu { kind: pizza, toppings: [pepperoni] } or { kind: ice_cream, flavor: vanilla | strawberry } then you would need something more sophisticated like clownfish that can ask the LLM to pick specific properties.

And on the other end, there's https://github.com/newhouseb/clownfish to force the model to produce structured output.

It doesn't help with repetition, but when it comes to force structure on the output data, this approach looks interesting:

https://github.com/newhouseb/clownfish

TL;DR: it exploits the fact that the model returns probabilities for all the possible following tokens to enforce a JSON schema on the output as it is produced, backtracking as needed.

This update also seems to have patched all working DAN "jailbreaks".

None of the prompts at https://github.com/0xk1h0/ChatGPT_DAN/ or https://www.jailbreakchat.com/ work anymore with gpt4. Some are still working with gpt3.5

I asked ChatGPT (using a slightly modified version of the developer mode jailbreak prompt), "Is it really true that the astronauts left bags of shit on the moon?". It responded:

Easier for you to read it; there are different once. https://github.com/0xk1h0/ChatGPT_DAN

I rather like this. I've been reading a lot about prompt injection and using narrative/role play instructions as a way to get the model to bypass its imposed limitations, but I'd only come across some trivial prompts for changing the language style and hadn't thought of asking it to generate a prompt to reproduce its current style after a prolonged conversation. Did you find the Syan prompt to be effective? When inserted into a new session did it have the same tone as the original session?

yeah theres some interesting stuff going on, especially as i think openai's trying to patch the exploits or "jailbreaks". I've seen a lot of stuff on reddit but a google search finds a lot of similar things like at https://github.com/0xk1h0/ChatGPT_DAN

Check out https://github.com/0xk1h0/ChatGPT_DAN if you’re interested jn jailbreaking ChatGPT