evals VS clownfish

Fair question.

Evaluate refers to the phase after training to check if the training is good.

Usually the flow goes training -> evaluation -> deployment (what you called inference). This project is aimed for evaluation. Evaluation can be slow (might even be slower than training if you're finetuning on a small domain specific subset)!

So there are [quite](https://github.com/microsoft/promptbench) [a](https://github.com/confident-ai/deepeval) [few](https://github.com/openai/evals) [frameworks](https://github.com/EleutherAI/lm-evaluation-harness) working on evaluation, however, all of them are quite slow, because LLM are slow if you don't have infinite money. [This](https://github.com/open-compass/opencompass) one tries to speed up by parallelizing on multiple computers, but none of them takes advantage of the fact that many evaluation queries might be similar and all try to evaluate on all given queries. And that's where this project might come in handy.

OpenAI open sourced their evals framework. You can use it to evaluate different models but also your entire prompt chain setup. https://github.com/openai/evals

They also have a registry of evals built in.

"What if" is all these "existential risk" conversations ever are.

Where is your evidence that we're approaching human level AGI, let alone SuperIntelligence? Because ChatGPT can (sometimes) approximate sophisticated conversation and deep knowledge?

How about some evidence that ChatGPT isn't even close? Just clone and run OpenAI's own evals repo https://github.com/openai/evals on the GPT-4 API.

It performs terribly on novel logic puzzles and exercises that a clever child could learn to do in an afternoon (there are some good chess evals, and I submitted one asking it to simulate a Forth machine).

Ah well, I think you just got lucky then, I did the same with the survey. I'll be compulsively checking mine all day today lol. People on Reddit like to say that if you did an Eval which is basically a performance test natively run using code on GPT models, then OpenAI is more likely to favor you when they’re releasing new features. If ydk, then I guess that answers that.

You can get GPT 4 access by submitting an eval if gets merged (https://github.com/openai/evals). Here's the one that got me access[1]

Although from the blog post it looks like they're planning to open up to everyone soon, so that may happen before you get through the evals backlog.

1: https://github.com/openai/evals/pull/778

来源：GitHub - openai/evals: Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks. 8

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.