251–260 of 315 posts
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It’s starting to feel like LLMs are to “classical” software engineering what quantum physics was to classical physics
How so? I’m not quite following the analogy.
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Truly.. one of the greatest minds in our ML era. Don't get caught up on the format :)
I just don't find it an effective way of learning personally. I didn't expect this to be so controversial - different people learn differently.
I haven’t done tonnes of courses so there might be better. But this is good as a free one.
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Your comment here doesn’t feel like it’s in good faith, but there’s a good chance I’m misreading it.
I'm serious that the similarities between the papers are superficial. I don't think it's fair of you to criticize the authors for not citing some obscure preprint, when that manuscript itself neglected to cite decades of prior, relevant work.
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?
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Maybe it's just me, but I'm not doing anything that calls itself 'zero to hero'. Would love some good resources (preferably textbook, or at least written) on LLMs though. I don't even understand the link to 'generative' image/video AI, which seems to have exploded at roughly the same time and surely isn't a coincidence. I studied a little (literally 'intro to') ML at university, about enough to grok it as an applicat…
Since you are "judging a book by its cover", or this case a name. This might interest you, that karpathy was co-founding developer of OpenAI, left to work at Tesla to head their AI development for 5-ish years and now back at OpenAI. I can understand that you might be interested in book form only, I was lile this for the longest time, until I bumped into some really high quality video series that changed my mind to be…
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100% have observed the same over many tests. No loss in fidelity when responding in spoken language style of formatting but using json is disastrous.
While not ideal, could a workaround be to ask in spoken language first, and then ask to format it in JSON?
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100% have observed the same over many tests. No loss in fidelity when responding in spoken language style of formatting but using json is disastrous.
Using OpenAI Function Calls or asking for JSON in the prompt?
I can make GPT4 return valid JSON simply by providing examples in the system message. This works nine times out of ten. But it's still probabilistic, and nine times out of ten isn't good enough. Occasionally it will hallucinate responses like this: {"key1": "value1", "key2": "value2" for i in range(n)} Re-prompting with the parsing error message is usually enough to get it on the second try. But escaping double-quote…
>I can make GPT4 return valid JSON simply by providing examples in the system message. This works nine times out of ten But you can do both. For my current use case of extracting information from articles, I have a json schema + one/two example articles along with their correct answers. This increases token costs but 3.5 is so cheap that it doesn't matter and for 4 you can use batching to decrease token cost per arti…
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I'm serious that the similarities between the papers are superficial. I don't think it's fair of you to criticize the authors for not citing some obscure preprint, when that manuscript itself neglected to cite decades of prior, relevant work.
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 genu…
Not in the slightest. Caching the logit masks and applying the right one based on where you are in your grammar is obvious. This is what I'd expect some bright undergrads to come up with for a class project. This manuscript could've been a blog post.
Although arXiv is displacing some traditional publishing, I think it's a little silly to try to hold it to the same standards.
I saw your argument for why you think it's relevant and I think you're overstating the case. There are a _heap_ of papers they could've cited.
As an aside, when can we stop citing _Attention is All You Need_?
How is this different from generating such things without an LLM? In other words picking random valid tokens from the grammar via fuzzing or similar techniques.
However, anyone who's tried to build a system like this on GPT or other LLM soon learns that they don't always do as they're told, and it can be hard to get them to return valid JSON or correct instructions translation reliably. Sometimes, they make stuff up that has nothing to do with your system.
OpenAI has a solution to this with their new function calling API, by introducing models fine-tuned to return JSON, but they still can't make guarantees.
Outlines seems to be a neat approach to constrain an LLM to return JSON, or any grammar, reliably.
Hi, remilouf. You say that your background is in "probabilistic, relational and symbolic programming". In that case I suspect you understand that it is no problem to generate text from a regular or context-free grammar, or really any level of grammar. For example, you can do that very easily in Prolog (a relational language) given a grammar in Definite Clause Grammars notation. As far as I can tell your approach requ…