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        <title>Future Lens: Anticipating Subsequent Tokens from a Single Hidden State</title>
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        <namePart type="given">Koyena</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Future Lens: Anticipating Subsequent Tokens from a Single Hidden State
%A Pal, Koyena
%A Sun, Jiuding
%A Yuan, Andrew
%A Wallace, Byron
%A Bau, David
%Y Jiang, Jing
%Y Reitter, David
%Y Deng, Shumin
%S Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F pal-etal-2023-future
%X 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.
%R 10.18653/v1/2023.conll-1.37
%U https://aclanthology.org/2023.conll-1.37
%U https://doi.org/10.18653/v1/2023.conll-1.37
%P 548-560

Markdown (Informal)

[Future Lens: Anticipating Subsequent Tokens from a Single Hidden State](https://aclanthology.org/2023.conll-1.37) (Pal et al., CoNLL 2023)

• Future Lens: Anticipating Subsequent Tokens from a Single Hidden State (Pal et al., CoNLL 2023)

ACL

• Koyena Pal, Jiuding Sun, Andrew Yuan, Byron Wallace, and David Bau. 2023. Future Lens: Anticipating Subsequent Tokens from a Single Hidden State. In Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL), pages 548–560, Singapore. Association for Computational Linguistics.