But then at the end it says it’s parody. Maybe HN title should say it’s a joke.
you can swith to a better model for lower error rate.
I've found that using structured outputs solves this problem much better. Instead of letting a model generate only "A", "B" or "C" and looking at the probs, have it directly generate "Legitimate", "Spam" or "Phishing" or any other pre-defined option from a set of multi-token sequences. Behind the scenes it boils down to something quite similar, but you're not running into the risk that the model actually wanted to say "A phishing attempt seems likely, so answer (C) is correct.", which would lead "A" to have the highest probability in the first token. You can even use a reasoning budget this way either via inherent reasoning or a free-form part preceding the remaining output structure. You can also have it assign probabilities (either in words or numbers) using more complex output structures, but I would not rely on them much more than the token logprobs (they can still be quite good though).
Actually to me it sounds it could be benchmarked if this kind of effect exists in the first place.
If you're comparing with something, you need to state 'fast' in relative terms. Jev is definitely fast, and if this Python takes the same time to get a decision then it's also fast. If it's 100* slower than Jev though, you shouldn't be calling it 'fast', because relatively speaking it's really, really slow.
So, fast in the LLM space and comparable with Jev.
Speed and cost are obvious reasons, but isn’t this a tradeoff?
To completely squash the issue, a few cheap LoRa iterations will do the trick just fine.
I think we can all agree that Jev is not rocket science. It's a good idea executed well, with marketing that might have been a tad too bold
I ran some tests using GPT-4 to do some basic classification a couple years ago. On ambiguous options which had to be escalated to a human, the LLM would regularly output something like a 99.8% probability, compared to 99.99% for a correct answer.
https://til.simonwillison.net/llms/llama-cpp-python-grammars
heaney-555•42m ago
thephyber•20m ago
ricardobeat•18m ago