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If embeddings are roughly the equivalent of a hash at least insofar as they transform a large input into some kind of "content-addressed distillation" (ignoring

If embeddings are roughly the equivalent of a hash at least insofar as they transform a large input into some kind of "content-addressed distillation" (ignoring the major difference that a hash is opaque whereas an embedding has intrinsic meaning), has there been any research done on "cracking" them? That is, starting from an embedding and working backwards to generate a piece of text that is semantically close by? I could imagine an LLM inference pipeline where the next token ranking includes its similarity to the target embedding, or perhaps instead the change in direction towards/away from the desired embedding that adding it would introduce. Put another way, the author gives the example: > embedding("king") - embedding("man") + embedding("woman") ≈ embedding("queen") What if you could do that but for whole bodies of text? I'm imagining being able to do "semantic algebra" with whole paragraphs/articles/books. Instead of just prompting an LLM to "adjust the tone to be more friendly", you could have the core concept of "friendly" (or some more nuanced variant thereof) and "add" it to your existing text, etc.

luke-stanley 1y ago
"starting from an embedding and working backwards to generate a piece of text that is semantically close by?" Apparently this is called embedding inversion and Universal Zero-shot Embedding Inversion https://arxiv.org/abs/2504.00147 https://arxiv.org/abs/2504.00147 Going incrementally closer and closer to the target with some means to vary seems to be the most general way, there are lots of ways to be more optimal though. Image diffusion with CLIP embeddings and such is kinda related too.
- luke-stanley 1y ago
  I meant to say: Apparently this is called "embedding inversion", and that "Universal Zero-shot Embedding Inversion" is a related paper that covers a lot of the basics. Recently I learned that a ArXiv RAG agent by ArXiv Labs is a really cool way for people wanting to find out about research: https://www.alphaxiv.org/assistant https://www.alphaxiv.org/assistant Though I had ran into "inversion" before, the AlphaXiv Assistant introduced me to "embedding inversion".
jerjerjer 1y ago
> If embeddings are roughly the equivalent of a hash Embeddings are roughly the equivalent of fuzzy hashes.
- quantadev 1y ago
  A hash is a way of mapping a data array to a more compact representation that only has one output with the attribute of uniqueness and improbability of collision. This is the opposite of what embeddings are for, and what they do. Embeddings are a way of mapping a data array to a different (and yes smaller) data array, but the goal is not to compress into one thing, but to spread out into an array of output, where each element of the output has meaning. Embeddings are the exact opposite of hashes. Hashes destroy meaning. Embeddings create meaning. Hashes destroy structure in space. Embeddings create structures in space.
  - nighthawk454 1y ago
    A hash function in general is only a function that maps input to a fixed-length output. So embeddings are hash functions. You’re probably thinking of cryptographic hashes, where avoiding collisions is important. But it’s not intrinsic. For example, Locality Sensitive Hashing where specific types of collisions are encouraged.
    - deleted 1y ago
      [deleted]
    - quantadev 1y ago
      Yes, some hash functions are intended to have collisions (like hash algorithms that are designed to put things into 'buckets' for searching for example). And you're correct to notice that by mentioning improbability of collision I'm talking about strong hashes in that sentence. But you can take my words literally nonetheless. When I say "hash" I mean all kinds of hashes. Strong and weak. The existence of weaker hash algos actually moves you further away from your assertion (that semantic vectors are hashes) than closer to it. Weak hashes is about a small finite number of buckets in one dimension. Semantic vectors are an infinite continuum of higher dimensional space locations. These two concepts are therefore the exact opposite.
smokel 1y ago
Not an expert in the field, but apparently there has been some research into this. It's called inference-time intervention [1], [2]. [1] "Steering Language Models With Activation Engineering", 2023, https://arxiv.org/abs/2308.10248 https://arxiv.org/abs/2308.10248 [2] "Multi-Attribute Steering of Language Models via Targeted Intervention", 2025, https://arxiv.org/pdf/2502.12446 https://arxiv.org/pdf/2502.12446
kaycebasques 1y ago
Follow-up question based on your semantic algebra idea. If you can start with an embedding and generate semantically similar text, does that mean that "length of text" is also one of the properties that embeddings capture?
- jas8425 1y ago
  I'm 95% sure that it does not, at least as far as the the essence of any arbitrary concept does or doesn't relate to the "length of text". Theoretically you should just as easily be able to add or subtract embeddings from a book as a tweet, though of course the former would require more computation than the latter.

luke-stanley 1y ago

"starting from an embedding and working backwards to generate a piece of text that is semantically close by?" Apparently this is called embedding inversion and Universal Zero-shot Embedding Inversion https://arxiv.org/abs/2504.00147 https://arxiv.org/abs/2504.00147 Going incrementally closer and closer to the target with some means to vary seems to be the most general way, there are lots of ways to be more optimal though. Image diffusion with CLIP embeddings and such is kinda related too.

luke-stanley 1y ago
I meant to say: Apparently this is called "embedding inversion", and that "Universal Zero-shot Embedding Inversion" is a related paper that covers a lot of the basics. Recently I learned that a ArXiv RAG agent by ArXiv Labs is a really cool way for people wanting to find out about research: https://www.alphaxiv.org/assistant https://www.alphaxiv.org/assistant Though I had ran into "inversion" before, the AlphaXiv Assistant introduced me to "embedding inversion".

jerjerjer 1y ago

> If embeddings are roughly the equivalent of a hash Embeddings are roughly the equivalent of fuzzy hashes.

quantadev 1y ago
A hash is a way of mapping a data array to a more compact representation that only has one output with the attribute of uniqueness and improbability of collision. This is the opposite of what embeddings are for, and what they do. Embeddings are a way of mapping a data array to a different (and yes smaller) data array, but the goal is not to compress into one thing, but to spread out into an array of output, where each element of the output has meaning. Embeddings are the exact opposite of hashes. Hashes destroy meaning. Embeddings create meaning. Hashes destroy structure in space. Embeddings create structures in space.
- nighthawk454 1y ago
  A hash function in general is only a function that maps input to a fixed-length output. So embeddings are hash functions. You’re probably thinking of cryptographic hashes, where avoiding collisions is important. But it’s not intrinsic. For example, Locality Sensitive Hashing where specific types of collisions are encouraged.
  - deleted 1y ago
    [deleted]
  - quantadev 1y ago
    Yes, some hash functions are intended to have collisions (like hash algorithms that are designed to put things into 'buckets' for searching for example). And you're correct to notice that by mentioning improbability of collision I'm talking about strong hashes in that sentence. But you can take my words literally nonetheless. When I say "hash" I mean all kinds of hashes. Strong and weak. The existence of weaker hash algos actually moves you further away from your assertion (that semantic vectors are hashes) than closer to it. Weak hashes is about a small finite number of buckets in one dimension. Semantic vectors are an infinite continuum of higher dimensional space locations. These two concepts are therefore the exact opposite.

smokel 1y ago

Not an expert in the field, but apparently there has been some research into this. It's called inference-time intervention [1], [2]. [1] "Steering Language Models With Activation Engineering", 2023, https://arxiv.org/abs/2308.10248 https://arxiv.org/abs/2308.10248 [2] "Multi-Attribute Steering of Language Models via Targeted Intervention", 2025, https://arxiv.org/pdf/2502.12446 https://arxiv.org/pdf/2502.12446

kaycebasques 1y ago

Follow-up question based on your semantic algebra idea. If you can start with an embedding and generate semantically similar text, does that mean that "length of text" is also one of the properties that embeddings capture?

jas8425 1y ago
I'm 95% sure that it does not, at least as far as the the essence of any arbitrary concept does or doesn't relate to the "length of text". Theoretically you should just as easily be able to add or subtract embeddings from a book as a tweet, though of course the former would require more computation than the latter.