A non-anthropomorphized view of LLMs

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.

IMHO, anthrophormization of LLMs is happening because it's perceived as good marketing by big corporate vendors.

People are excited about the technology and it's easy to use the terminology the vendor is using. At that point I think it gets kind of self fulfilling. Kind of like the meme about how to pronounce GIF.

I think anthropomorphizing LLMs is useful, not just a marketing tactic. A lot of intuitions about how humans think map pretty well to LLMs, and it is much easier to build intuitions about how LLMs work by building upon our intuitions about how humans think than by trying to build your intuitions from scratch.

Would this question be clear for a human? If so, it is probably clear for an LLM. Did I provide enough context for a human to diagnose the problem? Then an LLM will probably have a better chance of diagnosing the problem. Would a human find the structure of this document confusing? An LLM would likely perform poorly when reading it as well.

Re-applying human intuitions to LLMs is a good starting point to gaining intuition about how to work with LLMs. Conversely, understanding sequences of tokens and probability spaces doesn't give you much intuition about how you should phrase questions to get good responses from LLMs. The technical reality doesn't explain the emergent behaviour very well.

I don't think this is mutually exclusive with what the author is talking about either. There are some ways that people think about LLMs where I think the anthropomorphization really breaks down. I think the author says it nicely:

> The moment that people ascribe properties such as "consciousness" or "ethics" or "values" or "morals" to these learnt mappings is where I tend to get lost.

You think it's useful because Big Corp sold you that lie.

Wait till the disillusionment sets in.