Uncovering mesa-optimization algorithms in Transformers
- URL: http://arxiv.org/abs/2309.05858v1
- Date: Mon, 11 Sep 2023 22:42:50 GMT
- Title: Uncovering mesa-optimization algorithms in Transformers
- Authors: Johannes von Oswald, Eyvind Niklasson, Maximilian Schlegel, Seijin
Kobayashi, Nicolas Zucchet, Nino Scherrer, Nolan Miller, Mark Sandler, Blaise
Ag\"uera y Arcas, Max Vladymyrov, Razvan Pascanu, Jo\~ao Sacramento
- Abstract summary: We show that the strong performance of Transformers stems from an architectural bias towards mesa-optimization.
We propose a novel self-attention layer, the mesa-layer, that explicitly and efficiently solves optimization problems specified in context.
- Score: 27.180287282321576
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Transformers have become the dominant model in deep learning, but the reason
for their superior performance is poorly understood. Here, we hypothesize that
the strong performance of Transformers stems from an architectural bias towards
mesa-optimization, a learned process running within the forward pass of a model
consisting of the following two steps: (i) the construction of an internal
learning objective, and (ii) its corresponding solution found through
optimization. To test this hypothesis, we reverse-engineer a series of
autoregressive Transformers trained on simple sequence modeling tasks,
uncovering underlying gradient-based mesa-optimization algorithms driving the
generation of predictions. Moreover, we show that the learned forward-pass
optimization algorithm can be immediately repurposed to solve supervised
few-shot tasks, suggesting that mesa-optimization might underlie the in-context
learning capabilities of large language models. Finally, we propose a novel
self-attention layer, the mesa-layer, that explicitly and efficiently solves
optimization problems specified in context. We find that this layer can lead to
improved performance in synthetic and preliminary language modeling
experiments, adding weight to our hypothesis that mesa-optimization is an
important operation hidden within the weights of trained Transformers.
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