AI to study my DSL and then output it?

There are a couple different approaches:

- Use multi-shot prompting with something like guardrails to try prompting a commercial model until it works. [1]

- Use a local model with a final layer that steers token selection towards syntactically valid tokens [2]

[1] https://github.com/ShreyaR/guardrails

[2] "Structural Alignment: Modifying Transformers (like GPT) to Follow a JSON Schema" @ https://github.com/newhouseb/clownfish (full disclosure: this is my work)

Honestly ChatGPT has worked well for things like this in my experience. If you can fit enough examples within a prompt, you may not need anything special.

LLMs like GPT-4 'natively' speak certain syntaxes very well - e.g. Python, JSON. I'd suggest you want to take advantage of that, if at all possible, rather than embark on training or fine tuning your own LLM.

If you have a particular data structure you want to have the LLM generate or manipulate, which there aren't large quantities of in the training set, you might want to consider writing a translator that will translate it into a format the LLM natively 'speaks', using the LLM on that, and then translating back into your DSL.

Going this direction and also adding examples in some sort of vector store, as others have suggested, could be a good direction.

The best answer, by far, would be ChatGPT and GPT4 with some well-written prompts.

I'd be super impressed if any other approach worked as well and would fall under the category of "easy". Keep us updated on what you go with!

See

https://huggingface.co/blog/codeparrot

for some idea of how to train a code generator.

On https://flowchart.fun I found that I got better overall results by asking GPT for an intermediate syntax that it was less likely to mess up (and easier for me to parse), and then parsing and transforming that syntax to my DSL. The relevant code: https://github.com/tone-row/flowchart-fun/blob/main/api/prom...

We have a similar issue - we have a domain-specific schema that we want GPT4 to author SQL for. The challenge for us is that a full explanation of everything in the schema absolutely blows out the token limits.

Right now, we are playing around with the idea of using a classification layer to detect which schema elements are likely involved, and then dynamically including explanations for those elements in the final prompt.

Our attempts at fine tuning ended after about 2 weeks of struggling. I don't think it's viable for a certain range of domain-specific tasks.

I've had good success teaching GPT4 a language interactively: provide documentation, examples then asked it to generate examples of increasing complexity and correct it if it's wrong.

See previous comment here: https://news.ycombinator.com/item?id=35447368

Langchain with a vectorstore of examples of your DSL. https://python.langchain.com/en/latest/modules/indexes/vecto...

What have you tried so far?

This is very interesting.

I’m still noodling on how to send a full page screenshot to a model and get it to return the individual images (or the bounds of them) in the page.

txtai accomplished a similar task by fine tuning a very small t5 model, notebook with usage samples (training code has to be somewhere near)

https://github.com/neuml/txtai/blob/master/examples/33_Query...

AI today is not intelligent, it is just a sophisticated generator using patterns it was trained on.

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