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A non-anthropomorphized view of LLMs

(addxorrol.blogspot.com)
475 points zdw | 3 comments | | HN request time: 0.001s | source
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barrkel ◴[] No.44485012[source]
The problem with viewing LLMs as just sequence generators, and malbehaviour as bad sequences, is that it simplifies too much. LLMs have hidden state not necessarily directly reflected in the tokens being produced and it is possible for LLMs to output tokens in opposition to this hidden state to achieve longer term outcomes (or predictions, if you prefer).

Is it too anthropomorphic to say that this is a lie? To say that the hidden state and its long term predictions amount to a kind of goal? Maybe it is. But we then need a bunch of new words which have almost 1:1 correspondence to concepts from human agency and behavior to describe the processes that LLMs simulate to minimize prediction loss.

Reasoning by analogy is always shaky. It probably wouldn't be so bad to do so. But it would also amount to impenetrable jargon. It would be an uphill struggle to promulgate.

Instead, we use the anthropomorphic terminology, and then find ways to classify LLM behavior in human concept space. They are very defective humans, so it's still a bit misleading, but at least jargon is reduced.

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gugagore ◴[] No.44485190[source]
I'm not sure what you mean by "hidden state". If you set aside chain of thought, memories, system prompts, etc. and the interfaces that don't show them, there is no hidden state.

These LLMs are almost always, to my knowledge, autoregressive models, not recurrent models (Mamba is a notable exception).

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barrkel ◴[] No.44485271[source]
Hidden state in the form of the activation heads, intermediate activations and so on. Logically, in autoregression these are recalculated every time you run the sequence to predict the next token. The point is, the entire NN state isn't output for each token. There is lots of hidden state that goes into selecting that token and the token isn't a full representation of that information.
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1. brookst ◴[] No.44485360[source]
State typically means between interactions. By this definition a simple for loop has “hidden state” in the counter.
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2. ChadNauseam ◴[] No.44485945[source]
Hidden layer is a term of art in machine learning / neural network research. See https://en.wikipedia.org/wiki/Hidden_layer . Somehow this term mutated into "hidden state", which in informal contexts does seem to be used quite often the way the grandparent comment used it.
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3. lostmsu ◴[] No.44486332[source]
It makes sense in LLM context because the processing of these is time-sequential in LLM's internal time.