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Comment by brigadier132 | Hacker News Reader

I disagree, the embeddings are what are used by the llms themselves to produce relevant output and the output is relevant ergo the embeddings do produce relevant output via similarity search

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You probably aren’t using an LLM for your text embeddings for document retrieval (they don’t perform as well as specialist embedding models[0]), and even if they did, you have an embedding about a bare document, without any context of what you are trying to get out of it. If you were to add your context in and then get an embedding, you would get a different answer. As your query gets specific, irrelevant aspects of the embedding space can overwhelm the similarity function, leading to irrelevant answers that are still semantically similar.

[0] https://huggingface.co/spaces/mteb/leaderboard

The recent SILO-LM paper has a slightly different approach: rather than using input embeddings and prompting the LLM with documents, it searches the database according to the LLM's output embedding and uses KNN search to skew the output embedding vector before token generation. Done that way round, using LLM embeddings outperforms RAG, allegedly.

They did it with a custom language model. I really want to give this a try with llama2 embeddings but haven't had the bandwidth yet (and llama2's embedding vectors are inconveniently huge, but that's a different problem).

Interesting! I’ll have to look into that.

Consider the extreme case: when I ask a question about X, then a page with just the questions about X will get the highest similarity. But what I want in terms of relevance for the answer is a page with a little bit about X and lots of surrounding context that answers the question. By definition the extra context will likely lower the similarity.

Not if you're using ANN. In some cases that will be very similar to exhaustive search but in other cases you'll get results that you don't want. You also need embeddings that distribute things mostly evenly across the embedding space (not all will).

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