Future Lens: Anticipating Subsequent Tokens from a Single Hidden State (2311.04897v1)

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.

• The paper shows that hidden states can predict multiple future tokens, achieving over 48% accuracy with learned prompt methods.
• It evaluates techniques like direct vocabulary prediction and causal interventions to decode future token information.
• The introduction of the Future Lens tool provides a novel visualization for interpreting hidden state contributions in token prediction.

Overview of "Future Lens: Anticipating Subsequent Tokens from a Single Hidden State"

The presented paper investigates the ability of hidden state vectors in LLMs to predict tokens multiple steps ahead, rather than solely the next token. This paper uses GPT-J-6B to empirically evaluate the extent to which individual hidden states encode information relevant for predicting subsequent tokens. The researchers propose methods for decoding these predictions and introduce the "Future Lens" visualization tool to illustrate these findings.

Methodology

The authors employ several methodologies to examine if a single hidden state can predict tokens at positions greater than $t+2$. These methods are:

1. Direct Vocabulary Prediction: A linear model trained to predict future token distributions directly from a hidden state.
2. Linear Model Approximation: This extends the direct vocabulary prediction by anticipating future hidden states, using a learned linear transformation before predicting tokens.
3. Fixed Prompt Causal Intervention: By transplanting a hidden state into a different context, this approach evaluates its influence on generating subsequent tokens from the original context.
4. Learned Prompt Causal Intervention: It optimizes context prompts to maximize subsequent token prediction accuracy when inserted into another context.

These methods provide insight into whether hidden states contain predictive information beyond the next-token prediction task typically employed in autoregressive LLMs.

Key Results and Findings

The experiments reveal that portions of the hidden states at certain layers, particularly middle ones, encode substantial information about upcoming tokens, achieving over 48% accuracy under optimal circumstances. The "Learned Prompt" method effectively extracts this information, outperforming baseline models such as bigram models.

The precision and surprisal metrics demonstrate a superior performance by the learned prompt method, indicating its efficacy in uncovering future token information encoded within individual hidden states.

Implications

These results imply that hidden states in LLMs encapsulate rich information about sequences extending beyond immediate predictions. This discovery has practical implications for natural language processing tasks, potentially improving efficiency in LLMing and influencing future model architectures.

Moreover, the introduction of the "Future Lens" provides a tool for visualizing model internals, offering insights into hidden state functionalities and sequence prediction processes. This tool could be revolutionary in understanding model predictions and guiding efforts to interpret and manipulate predictions for specific applications.

Future Directions

Future research could expand upon these findings by exploring other LLMs, examining various architectures, and investigating further applications of the Future Lens. Additionally, extending this approach to predict even further into token sequences could enhance understanding of long-range dependencies in model predictions.

By demonstrating that hidden states can encode multiple future tokens, this work invites further investigation into the mechanisms underlying this phenomenon and its potential use in refining model efficiency and prediction accuracy.