5 ms·
Don't classify, hallucinate
- eka1 2mo agoDid you validate this by running a A/B test? Main question is were you able to classify back into your known categories correctly all the time, or did the errors compound from the llm hallucination plus embedding search
- softwaredoug 2mo agoUsing a Nano model, a tad worse than shipping a vocabulary to a larger OpenAI model. (And it’s an huge improvement on not classifying the queries at all). But no classification is perfect. In search in particular, you will also want to have places for manual intervention for high priority queries.
- jingpostmedia 2mo ago[flagged]
- tjonesit 2mo ago[flagged]
- microgpt2 2mo ago[dead]
- VladVladikoff 2mo agoEh, maybe you should keep both paths. When LLMs eventually crawl the site to feed back to agentic shoppers, maybe they logically follow the more truncated less decorated path.
- estetlinus 2mo agoI 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.
- thatjoeoverthr 2mo agoSmart! 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.
- piterrro 2mo agoI would propose the following, query vector store for 10 closest categories based on a query, feed it to an LLM, in the prompt ask it to produce a single digit 0-9 representing the number of the most appropriate choice. Use plain text prompt, dont inflate token count with JSON. There you go, you just drastically reduced the output pricing. Additionally you could experiment with a reranker instead of an LLM or after reranking take top-3 results and then feed to LLM as input in order to reduce input token costs.
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- cesargstn 2mo agogood this yeah
- deleted 2mo ago[deleted]
- Colegno 2mo agoIsn'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.
- fastball 2mo agoA 2s LLM call is pretty slow.
- gadflyinyoureye 2mo agoTry using Digital Ocean. Minutes spent on inference.
- quixoticaxolotl 2mo agoThey 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.
- nullsanity 2mo ago[dead]
- amelius 2mo agoCan anyone explain why LLMs are so bad at finding products (their webpages) with given specifications? You'd think they would have solved it by now.
- braiamp 2mo agoBecause that's structured data and structured data is usually hidden away from users _and_ machines. Product rarely want to be honest, unless it's B2B in a very competitive market (and even then!). So, yeah, it's not that they are bad, it's that there are few good sources of information. (Lets ignore for now that no one seems to agree to what should be the spec sheets)
- amelius 2mo agoAn LLM can read websites, right? And turn them into structured data.
- ashu1461 2mo agoIt can do that on run time, but it does not store data like that. The data is typically stored as embeddings in which it is hard to query data in a structured form. Example give me all products whose price is less than 200$ vs suggest me products for my spouse's birthday.
- amelius 2mo agoThen they shouldn't store the data as embeddings. Instead: use an LLM to build a large (old-school) database of products with all their specifications. The LLM can also build the schema for that database as it finds more data. Then use an LLM to query that database based on the user's specifications (+ add some intelligence to find nice suggestions for a birthday if wanted, but I'd consider that an extra).
- ashu1461 2mo agoWith agentic commerce protocol / unified commerce protocol open ai and gemini are trying to solve this problem. The idea is to make structured queries using these protocols which can be used to fetch top products matching the user needs instead of just relying on semantic search. https://developers.openai.com/commerce/specs/file-upload/products https://developers.openai.com/commerce/specs/file-upload/pro...
- apwheele 2mo agoThis is another riff on not embedding a full document, but doing a summarization of the document and embedding the summary for RAG. Nice usecase for high cardinality data!
- ashu1461 2mo agoHad stumbled on this library in the past https://github.com/aurelio-labs/semantic-router https://github.com/aurelio-labs/semantic-router I guess it is based on the same fundamentals as well.
- ipsod 2mo agoJust 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 2mo agoI’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.
- Majromax 2mo ago> In the notebook, I compute a MiniLM embedding of every real Wayfair classification. I compute the embedding of the fake, hypothetical embedding from the LLM. I then dot product the fake embedding into the real ones to find the most similar. Producing: [the right answer] Isn't this begging the question that the hallucinated classification will be more selective with respect to the real schema than the query itself? What would the dot product of <E(search query), E(schema)> have given? Even if that is too vague, smaller LLMs are capable rerankers; return the top N matching true categories and ask for a contextual ordering.
- softwaredoug 2mo agoYes what you're describing is a classic way of doing query understanding. I've found, though, getting it in the language of the vocabulary has generally improved performance. Further, when searching for "blue shoes" you want to separate the color from the item type. So its useful to have a dumb LLM do this for you. And with the LLM in the loop, its further useful to get it into the language of the taxonomy to improve embedding retrieval accuracy. There are of course many ways to skin the cat here :)
- einpoklum 2mo agoIn the past, people would post advice on how to do something clever and useful yourself. Now, people post suggestions on how to talk out the side of their mouth to coax ther magic-8-ball slop generator to say something useful.
- sergiotapia 2mo agoThis is a really great trick, woah!
- amitpoonia19xyz 2mo agoThis is basically HyDE (Hypothetical Document Embeddings), no? I had tried this approach in the past, worked with limited success.
- memjay 2mo agoWe have this running in production. Can get pretty expensive and slow. We are trying to replace this with cheaper and faster methods that don’t hammer our LLM and elastic search endpoints as much.
- kaycebasques 2mo agohttps://arxiv.org/abs/2212.10496 https://arxiv.org/abs/2212.10496 for others like me hearing about HyDE for the first time
- pu_pe 2mo agoNice trick. Couldn't you embed the query though, compare it to the embedding of the categories, then ship only categories that are close to it in the prompt to a smaller model?
- tantalor 2mo agoYeah I had the same question. What's the point of the intermediate step?
- vessenes 2mo agoAgreed that you almost certainly can just embed the original with most modern embedding models.
- softwaredoug 2mo agoYes absolutely that's another good trick. Even better is to search the corpus first with like naive BM25 / embedding search, aggregate over top N to get most representative categories, then have the LLM categorize in that set.
- kgeist 2mo agoIt's basically a variation of HyDE (Hypothetical Document Embeddings), and the rationale is that the embedding of the query is not necessarily close to the embedding of the answer. If you generate a hallucinated answer, it can line up with the actual document better (in the embedding space, via BM25, or hybrid). But honestly, it only works for common knowledge that's already in the LLM. If the target document contains very niche or private information, then the hallucinated answer's embedding can be even farther away than the query's.
- jaggederest 2mo agoI feel like querying for the smaller distance from the set of embeddings of both the query and a fake answer structure might solve that. If there's something very close to the query, it dominates, otherwise the fake answer is the guide. Plus, why not use an LLM to judge between them if we've got a token-burner all warmed up to start.
- claudiosf1 2mo agoSmart 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 2mo agoIf 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
- jobuildsstuff 2mo ago[flagged]
- smallnix 2mo agoSince you map each breadcrumb of the path, how do you deal with differing lengths that would be more appropriate?
- iandanforth 2mo agoNo? This is just giving up and hoping.
- chrisjj 2mo agoNo change from regular chatbot coding, then.
- motoxpro 2mo agoIs there a solution you are using to solve this that is more accurate and cost effective? I'm working through it now so would be curious
- runarberg 2mo agoIs scraping and putting this in a structured format too inaccurate or expensive?
- motoxpro 2mo agoThat's the whole problem. If you have tons (100s of thousands or more) of labels, then you have "structured" data, but how do you correctly classify that scraped item into the correct label? Putting all the labels into the LLM is super expensive per call when you have millions of items to classify. You can't reduce the number of labels becasue they are correctly organizes/structured. This class of problem exists in many different domains.
- runarberg 2mo ago100s of thousand? In that case I would label about a 100 by hand and train a supervised learning model. This problem has also been solved for 3 decades now.
- Terr_ 2mo agoAhhh, but the LLM processing is the kind corporate leadership and investors will actually agree to buy! Orginally I started writing that as sarcasm, and now I'm not quite so sure.
- arjie 2mo agoPrompt expansion of input to extra categories makes sense if your embedding isn’t working well. But on its own, why use the LLM at all? I think you could have demonstrated the original step first and then shown that it’s useful.
- Sharlin 2mo agoI can’t believe programming is now at the stage where advice like "first have the computer give you totally wrong answers, then just find a function that maps the wrong answers to the correct ones!" is a thing.
- agos 2mo agothe trick is that it's not totally wrong to start with
- addandsubtract 2mo agoIf there is no truth, there is no wrong.
- deleted 2mo ago[deleted]
- speerer 2mo agoThis is so similar to human decision-making though. First I my innate experience to approximate to what I expect is right, then I map that to the truth. It's the same for so many things: - reading documentation (what do I expect this function to be called?) - finding clothes in a shop (something long-sleeved and light) - picking the fridge for dinner - finding a book in the library... so many analogues where I'm not coming cold to a choice.
- willturman 2mo agoI can't believe people spend their lives finding lazier ways to classify a bunch of objects that will end up heaped in dormitory dumpsters across the US next spring.
- deleted 2mo ago[deleted]
- pie_flavor 2mo ago> On two occasions I have been asked, – "Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?" ... I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question. He clearly didn't know enough about vector embeddings.
- HarHarVeryFunny 2mo agoInteresting 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?
- kgeist 2mo agoA common case I have is when you don't have classifications to begin with. For example, you need to find what users complain about most. I take embeddings of all records, then cluster the embeddings into semantic groups, then ask an LLM to take a random sample from each clustered group and create a classification for that group. This method is sensitive to the thresholds (what is the maximum distance between embeddings for them to be still considered part of the same semantic group), so I run it all in an agentic loop where an agent tries different thresholds and clustering algorithms until it's satisfied with the result, plus it may deduplicate some groups. I run it all on self-hosted hardware, so it costs nothing to leave it running for, like, a night, and as a bonus, none of the corporate data leaves the office. I think a rigid set of manually created classifications may not capture all the possible classifications that can exist. Needs a review by a human, though.
- alexpotato 2mo agoI worked on spam classification for litigation targeting in the early days of CANSPAM [0] enforcement. We had a similar problem where you can literally millions of email that we were pretty sure came from only a limited set of bad actors. We first started classifying emails into buckets by From, mailserver relay chains etc as that's all we had to to go on. Over time, those buckets got linked to spammer signatures and then we narrowed down from there. Fascinating to see this happening nowadays with LLMs.
- nostrebored 2mo agoThe idea of distance thresholding models that are trained to satisfy an ordering constraint is a bit strange. The reason it's hard is that there isn't a threshold! You can slice and dice it a ton of different ways, but the significance of groups is incidental. It's a good starting point, but having done this a few times for a few companies it always seems like it needs substantial human review.
- sirnicolaz 2mo agoI wonder how more accurate this is compared to just doing embedding similarity of the query vector and the category labels
- ed 2mo agoNew 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 2mo agoWhat 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.
- ed 2mo agoRetrieval models are trained on query/document pairs. At inference you tell it which side the text is on via a prefix it learned during training. Which accomplishes the same thing as HyDE, but in the model instead of in text space. If the encoder has a query mode you skip the rewrite. Voyage has an example of this in `input_type` https://docs.voyageai.com/reference/embeddings-api https://docs.voyageai.com/reference/embeddings-api
- cimi_ 2mo agoI did something similar 10 years ago, but instead of llms I used word2vec to calculate a embeddings of product descriptions and map those to existing categories. The LLM approach is very likely better, but I'm curious what the cost difference is.
- otikik 2mo agoI don't know the exact syntax any more, but I expect this could be solved by a single sql query that uses "inexact but close" queries and a bunch of indexes (and perhaps tags) on each category.
- Terr_ 2mo agoLike some sort of Jaccard Index based on how many tags are shared? https://en.wikipedia.org/wiki/Jaccard_index https://en.wikipedia.org/wiki/Jaccard_index
- otikik 2mo agoI was thinking more along the lines of using text search [1] [2] [1] https://www.postgresql.org/docs/current/textsearch-intro.html https://www.postgresql.org/docs/current/textsearch-intro.htm... [2] https://www.elastic.co/docs/explore-analyze/query-filter https://www.elastic.co/docs/explore-analyze/query-filter
- sonofzork 2mo agoGarbage in, gold out?
- moezd 2mo agoTIL LLMs follow Cunningham's Law, or they claim to be.
- aleksiy123 2mo agoJeopardy clustering Pretty cool technique honestly. You could do it the other way as well right? If you had a list of categories you have the model to generate a sample query and then do embedding on that?
- pvillano 2mo agoI would try using structured outputs recursively. ``` Request 1: "brown coffee table: " + {Root Schema} => "Furniture" Request 2: "brown coffee table: Furniture / " + {Furniture Schema} => "Living Room Furniture" Request 3: "brown coffee table: Furniture / Living Room Furniture / " + {Living Room Furniture Schema} => "Coffee Tables" ``` Many more round trips, but classifying products is not a latency sensitive task.
- andai 2mo agoNice. I heard something similar years ago. Instead of doing a RAG search based on the question, first hallucinate a plausible answer, and then use that as the query...
- hahahaa 2mo agoDon't hallucinate, mask logits if possible?
- thisisnotauser 2mo agoThis is awesome, it's literally harnessing llms for creativity. There's got to be a deeper angle here to develop llms in this direction explicitly: exploring possibility space and then mapping that into reality as post processing / tooling, in lieu of training so heavily around reality.