Future Lens: Anticipating Subsequent Tokens from a Single Hidden State
Koyena Pal Jiuding Sun Andrew Yuan Byron C. Wallace David Bau
We conjecture that hidden state vectors corresponding to individual input
tokens encode information sufficient to accurately predict several tokens
ahead. More concretely, in this paper we ask: Given a hidden (internal)
representation of a single token at position $t$ in an input, can we reliably
anticipate the tokens that will appear at positions $\geq t + 2$? To test this,
we measure linear approximation and causal intervention methods in GPT-J-6B to
evaluate the degree to which individual hidden states in the network contain
signal rich enough to predict future hidden states and, ultimately, token
outputs. We find that, at some layers, we can approximate a model's output with
more than 48% accuracy with respect to its prediction of subsequent tokens
through a single hidden state. Finally we present a "Future Lens" visualization
that uses these methods to create a new view of transformer states.