Oh it’s not explicitly in the paper, but in Apple’s version they have an encoder/decoder with explicit latent space. This space would be much easier to work with and steerable than the hidden states we have in transformers.
With an explicit and nicely behaved latent space we would have a much better chance of finding a predictive “truth” neuron where intervention reveals deception 99% of the time even out of sample. Right now mechinterp research achieves much less, partly because the transformers have quite confusing activation spaces (attention sinks, suppressed neurons, etc).
I think what you’re saying is that because the output of the encoder is a semantic embedding vector per paragraph, that results in a coherent latent space that probably has nice algebraic properties (in the same sense that eg the Word2Vec embedding space does). Is that a good representation?
That does seem intuitively plausible, although I could also imagine that there might have to be some messy subspaces for meta-level information, maybe eg ‘I’m answering in language X, with tone Y, to a user with inferred properties Z’. I’m looking forward to seeing some concrete interpretability work on these models.
Yes, that’s exactly what I mean! If we have word2vec like properties, steering and interpretability would be much easier and more reliable. And I do think it’s a research direction that is prospective, but not certain.
Facebook also did an interesting tokenizer, that makes LLM’s operating in a much richer embeddings space: https://github.com/facebookresearch/blt. They embed sentences split by entropy/surprise. So it might be another way to test the hypothesis that a better embedding space would provide ice Word2Vec like properties.
Why would we expect that to be the case? (If the answer is in the Apple paper, just point me there)
Oh it’s not explicitly in the paper, but in Apple’s version they have an encoder/decoder with explicit latent space. This space would be much easier to work with and steerable than the hidden states we have in transformers.
With an explicit and nicely behaved latent space we would have a much better chance of finding a predictive “truth” neuron where intervention reveals deception 99% of the time even out of sample. Right now mechinterp research achieves much less, partly because the transformers have quite confusing activation spaces (attention sinks, suppressed neurons, etc).
I think what you’re saying is that because the output of the encoder is a semantic embedding vector per paragraph, that results in a coherent latent space that probably has nice algebraic properties (in the same sense that eg the Word2Vec embedding space does). Is that a good representation?
That does seem intuitively plausible, although I could also imagine that there might have to be some messy subspaces for meta-level information, maybe eg ‘I’m answering in language X, with tone Y, to a user with inferred properties Z’. I’m looking forward to seeing some concrete interpretability work on these models.
Yes, that’s exactly what I mean! If we have word2vec like properties, steering and interpretability would be much easier and more reliable. And I do think it’s a research direction that is prospective, but not certain.
Facebook also did an interesting tokenizer, that makes LLM’s operating in a much richer embeddings space: https://github.com/facebookresearch/blt. They embed sentences split by entropy/surprise. So it might be another way to test the hypothesis that a better embedding space would provide ice Word2Vec like properties.