Neel Nanda (of Google Deepmind - his part begins on page 33) discusses his opinions on the paper, and the small-scale replication he performed on an open-weight model.
It would be really cool if they could expose this information to customers somehow. Imagine:
- having a log of the most prominent J-space tokens during your customer support chatbot's interactions with a user, so you can have more introspection into why a particular outcome happened
- being able to detect certain thoughts associated with undesirable behavior (hallucinations, overstepping authority, lying, etc.) and trigger some sort of remediation (e.g. upgrading to a better model, redirecting to a human, forcing tool calls)
Presumably the rationale for the decision to abridge the thinking traces will ensure that they don’t; if this is real (and there’s no good reason to trust that it is yet) then it is the secret sauce.
As someone who is not an AI researcher, the paper itself is way over my head.
More interesting was the independent commentary paper they linked near the bottom: https://www-cdn.anthropic.com/files/4zrzovbb/website/cc4be24...
Neel Nanda (of Google Deepmind - his part begins on page 33) discusses his opinions on the paper, and the small-scale replication he performed on an open-weight model.
Is it scaling up of https://openreview.net/forum?id=w7LU2s14kE with some changes on where this method is applied?
I’m confused where in the weights the jspace is.
Anthropic theorize that middle layers in an LLM is a "J-Space" used to "think" about the future answer or about abstract concepts.
Their method is used to identify which tokens can appears in which layers of the model.
Tokens that are activated but not present in it's output maybe?
I too have confusion.
It would be really cool if they could expose this information to customers somehow. Imagine:
Presumably the rationale for the decision to abridge the thinking traces will ensure that they don’t; if this is real (and there’s no good reason to trust that it is yet) then it is the secret sauce.
Without using the term, they are using an information geometric approach.
But J-Space is much catchier. This is not a scientific paper, it's a promotional essay.
Maybe model performance could increase dramatically if we found a way to scale this up.