Don't classify, hallucinate

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• Hacker News
• 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.
  by ed
• Prompt 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.
  by arjie
• I 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.
  by cimi_
• Did 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
  by eka1
• > 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.

  by Majromax
• Nice 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?
  by pu_pe
• A 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.

  by kgeist
• I 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.
  by Sharlin