Looking Beyond the Top-1: Transformers Determine Top Tokens in Order

Daria Lioubashevski, Tomer M. Schlank, Gabriel Stanovsky, Ariel Goldstein

Keywords: mechanistic interpretability, transformer, large language model, efficient inference

Abstract:

Understanding the inner workings of Transformers is crucial for achieving more
accurate and efficient predictions. In this work, we analyze the computation performed
by Transformers in the layers after the top-1 prediction has become fixed, which has been
previously referred to as the “saturation event”. We expand the concept of saturation events
for top-k tokens, demonstrating that similar saturation events occur across language, vision,
and speech models. We find that these saturation events happen in order of the
corresponding tokens’ ranking, i.e., the model first decides on the top ranking token, then
the second highest ranking token, and so on. This phenomenon seems intrinsic to the
Transformer architecture, occurring across different architectural variants (decoder-only,
encoder-only, and to a lesser extent full-Transformer), and even in untrained Transformers.
We propose an underlying mechanism of task transition for this sequential saturation, where
task k corresponds to predicting the k-th most probable token, and the saturation events are
in fact discrete transitions between the tasks. In support of this we show that it is possible to
predict the current task from hidden layer embedding. Furthermore, using an intervention
method we demonstrate that we can cause the model to switch from one task to the next.
Finally, leveraging our findings, we introduce a novel token-level early-exit strategy, which
surpasses existing methods in balancing performance and efficiency.

Primary Area: interpretability and explainable AI

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Submission Number: 8119