Don't classify, hallucinate

Posted by softwaredoug 3 days ago

Don't classify, hallucinate(softwaredoug.com)

168 points | 71 commentspage 2

claudiosf1 4 hours ago|

Smart trick, but assumes the “dumb” llm is smart enough not to derail into an article about the lives of South American red ants. Obvious exaggeration, the point being outcomes should stay strictly within topic, avoid unrelated bloat and hit the target.

phoghed 4 hours ago|

If you use structured outputs they’ll usually stick to the program. Not to completely constrain the categories like TFA was saying, but something like

    { rationale, categories }

Where you don’t really care about the rationale but you’re using it as a pseudo thinking for models that don’t support it.

Luna is surprising capable and cheap, and I haven’t done this type of thing since before GPT 5 so might not be such a useful trick now

ipsod 5 hours ago||

Just this week I tried doing something similar with a nasty vibe-coded codebase I was trying to organize. I had Gemini Flash 3.6 classify each function/method in a similar way, giving a few plausible classifications for each (one agent per method).

It didn't end up being very useful - I ran a comparison where I just had a bigger agent do the organization in a more straightforward way, and that had better results.

I did find that Flash 3.6 High was >9x faster than Luna xhigh for this task, and got very similar results, though.

sheepscreek 5 hours ago|

I’ve read a few different accounts, including OpenAI’s own admission, that Terra Medium or higher will likely produce better results than Luna xhigh and cost about the same or less.

ed 3 hours ago||

New embedding models support queries, so you don’t need to hallucinate a document before finding the nearest neighbor. Curious how it compares to this approach since you’d get to skip the LLM altogether.

bonoboTP 2 hours ago|

What does it mean that an "embedding model supports queries"? An embedding model maps text to embedding vectors. You can always perform queries with such embedding vectors against a stored set of embeddings.

thatjoeoverthr 6 hours ago||

Smart! I've done the same trick for resolving extracted intents to selection.

But if accuracy matters, you can't rely on embedding sort to get a closet match. With a real test set they usually don't hold up under scrutiny.

Everything in AI is like this. You get an idea, try it once or twice, "LGTM" and you ship. Then it never survives contact reality.

Embedding sort gives you a better shortlist than the whole list, but you will probably want a heavier model to vet candidates.

HarHarVeryFunny 4 hours ago||

Interesting technique, but even if you're getting rid of hallucinations it seems there's still no guarantee of consistent classifications. If you need to do a semantic (embedding) search anyways, then how does this really help?

ashu1461 5 hours ago||

Had stumbled on this library in the past

https://github.com/aurelio-labs/semantic-router

I guess it is based on the same fundamentals as well.

sirnicolaz 4 hours ago||

I wonder how more accurate this is compared to just doing embedding similarity of the query vector and the category labels

estetlinus 6 hours ago||

I was in a project where we sent the whole taxonomy every request, 40k tokens + one article, ”plz classify”. This was before structured outputs. It was extremely expensive and still hallucinated. Good ol’ days.

smallnix 4 hours ago||

Since you map each breadcrumb of the path, how do you deal with differing lengths that would be more appropriate?

Colegno 5 hours ago|

Isn't search engines quicker than calling a LLM ? It might have a huge impact between a 20ms search engine call and a 2s LLM call for the end user.

quixoticaxolotl 5 hours ago||

They are already solving the problem with search engines, they're just using an LLM as a first pass to create better embeddings to run a similarity match on first. The difference in latency is likely made up for in accuracy.

fastball 5 hours ago|||

A 2s LLM call is pretty slow.

gadflyinyoureye 5 hours ago||

Try using Digital Ocean. Minutes spent on inference.

nullsanity 5 hours ago||

[dead]

More comments...