flashtext VS clownfish

I have some other comment on this thread where I point out why I don’t think it’s superficial. Would love to get your feedback on that if you feel like spending more time on this thread.

But it’s not obscure? FlashText was a somewhat popular paper at the time (2017) with a popular repo (https://github.com/vi3k6i5/flashtext). Their paper was pretty derivative of Aho-Corasick, which they cited. If you think they genuinely fucked up, leave an issue on their repo (I’m, maybe to your surprise lol, not the author).

Anyway, I’m not a fan of the whatabboutery here. I don’t think OG’s paper is up to snuff on its lit review - do you?

The library flashtext basically creates these tries based on keywords you give it.

Seems like https://github.com/vi3k6i5/flashtext would be better suited here.

I started developing this tool after using haystack. Pipelines are easier to build with cherche because of the operators. Also, cherche offers FlashText, Lunr.py retrievers that are not available in Haystack and that I needed for the project I wanted to solve. Haystack is clearly more complete but I think also more complex to use.

For the text part not requiring regex, https://github.com/vi3k6i5/flashtext might help

Spacy for parsing the Headlines, remove stop words etc. might be ok but I think the problem is quite narrow so a set of fixed regex searches might work quite well. If regex is too slow, try: https://github.com/vi3k6i5/flashtext

I would probably use something like [flashtext](https://github.com/vi3k6i5/flashtext) which should not be too hard to port to kotlin.

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.