synquid VS guidance

https://www.youtube.com/watch?v=HnOix9TFy1A

Links to more projects and papers by Prof. Polikarpova: https://cseweb.ucsd.edu/~npolikarpova/

I think this is one of the main ones she discusses in the talk: https://github.com/nadia-polikarpova/synquid

My first post was poorly written. I didn't mean to imply that taking pride in a job well done was bad. I don't think it is. I think programmers have plenty of reason to be happy when they do good work[1]--what I think is foolish is having visions of grandeur when it comes to programming, which in my opinion is what _why seemed to have based on his reasoning for quitting. I could be way off mark, but that's my take.

I mentioned this in another comment, but I think it also has to do with a confusion of categories. _why seemed to want recognition akin to that received by, e.g. Thomas Bernhard, Kafka, for something like shoes or his other software/libraries or general contributions to computing. But the issue is these things will always be utilities for specialists, and any aesthetic properties they have (elegant design, expression, etc.) are secondary to their functioning and they'll always be relegated to the dusty realm of specialists since the code is not the product--the software is. One can write code to create an aesthetic object that is enjoyed and revered by the masses, but I have a hard time envisioning a future in which the masses will ever enjoy and revere code or engineering for its own sake.

Pride was the wrong word to use and one I lazily reached for. After reading your comment, you've helped me realize that what I advise against is misapplication of expectations to different categories of things. _why seemed to want an aesthetic reception and legacy on a general, popular scale for work that is ultimately only a utility to the vast majority of the population and indeed, not even accessible to the population, and even if it were, I don't think many people would admire programming libs for fun--such a hobby will remain the lot of only enthusiasts. There is no pop coding like there is pop music.

[1]: Though I'd also argue that much of what you state taking pride in is not programming--which is just expressing ideas in programming languages--what you are talking about is engineering/design, which can be done perfectly well and separately from the programming part. we just happen to solve a lot of problems with computers these days so we need to express solutions for computers to consume and we tend to blend those responsibilities (we'll one day get to a point where the computers do most of the programming and we just design https://github.com/nadia-polikarpova/synquid)

Synquid synthesizes programs from refinement types, which are very similar in that you express a type-level predicate on the output using an expression which involves the input.

[1]: https://github.com/guidance-ai/guidance/tree/main

> The most obvious usage of this is forcing a model to output valid JSON

Isn't this something that Outlines [0], Guidance [1] and others [2] already solve much more elegantly?

0. https://github.com/outlines-dev/outlines

1. https://github.com/guidance-ai/guidance

2. https://github.com/sgl-project/sglang

Or combine it with something like llama.cpp's grammer or microsoft's guidance-ai[0] (which I prefer) which would allow adding some react-style prompting and external tools. As others have mentioned, instruct tuning would help too.

[0] https://github.com/guidance-ai/guidance

have been experimenting with guidance and lmql. a bit too early to give any well formed opinions but really do like the idea of constraining llm output.

Thanks for your comment.

I did not know about "Betteridge's law of headlines", quite interesting. Thanks for sharing :)

You raise some interesting points.

1) Safety: It is true that LVMs and LLMs have unknown biases and could potentially create unsafe content. However, this is not necessarily unique to them, for example, Google had the same problem with their supervised learning model https://www.theverge.com/2018/1/12/16882408/google-racist-go.... It all depends on the original data. I believe we need systems on top of our models to ensure safety. It is also possible to restrict the output domain of our models (https://github.com/guidance-ai/guidance). Instead of allowing our LVMs to output any words, we could restrict it to only being able to answer "red, green, blue..." when giving the color of a car.

2) Cost: You are right right now LVMs are quite expensive to run. As you said are a great way to go to market faster but they cannot run on low-cost hardware for the moment. However, they could help with training those smaller models. Indeed, with see in the NLP domain that a lot of smaller models are trained on data created with GPT models. You can still distill the knowledge of your LVMs into a custom smaller model that can run on embedded devices. The advantage is that you can use your LVMs to generate data when it is scarce and use it as a fallback when your smaller device is uncertain of the answer.

3) Labelling data: I don't think labeling data is necessarily cheap. First, you have to collect the data, depending on the frequency of your events could take months of monitoring if you want to build a large-scale dataset. Lastly, not all labeling is necessarily cheap. I worked at a semiconductor company and labeled data was scarce as it required expert knowledge and could only be done by experienced employees. Indeed not all labelling can be done externally.

However, both approaches are indeed complementary and I think systems that will work the best will rely on both.

Thanks again for the thought-provoking discussion. I hope this answer some of the concerns you raised

I've had a bit of trouble getting function calling to work with cases that aren't just extracting some data from the input. The format is correct but it was harder to get the correct data if it wasn't a simple extraction.

Hopefully OpenAI and others will offer something like https://github.com/guidance-ai/guidance at some point to guarantee overall output structure.

Failed validations will retry, but from what I've seen JSONSchema + generated JSON examples are decently reliable in practice for gpt-3.5-turbo and extremely reliable on gpt-4.