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.

利用隐藏状态向量进行预测模型，使用线性逼近和因果干预方法对 GPT-J-6B 网络中的隐藏状态进行评估，发现某些层的单一隐藏状态可以以超过 48% 的准确率近似模型输出，并提出了 “未来镜头” 可视化方法来呈现 Transformer 状态。