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Additional ones that come to mind now are: Transformer Feed-Forward Layers Are Key-Value Memories https://arxiv.org/abs/2012.14913 https://arxiv.org/abs/2012.1

Additional ones that come to mind now are: Transformer Feed-Forward Layers Are Key-Value Memories https://arxiv.org/abs/2012.14913 https://arxiv.org/abs/2012.14913 The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention https://arxiv.org/abs/2202.05798 https://arxiv.org/abs/2202.05798 https://github.com/neelnanda-io/TransformerLens https://github.com/neelnanda-io/TransformerLens

HarHarVeryFunny 3y ago
That's great - thank you!
HarHarVeryFunny 3y ago
Another piece of the puzzle seems to be transformer "induction heads" where attention heads in consecutive layers work together to provide a mechanism that is believed to be responsible for much of in-context learning. The idea is that earlier instances of a token pattern/sequence in the context are used to predict the continuation of a similar pattern later on. In the most simple case this is a copying operation such that an early occurrence of AB predicts that a later A should be followed by B. In the more general case this becomes A'B' => AB which seems to be more of an analogy type relationship. https://arxiv.org/abs/2209.11895 https://arxiv.org/abs/2209.11895 https://youtu.be/Vea4cfn6TOA https://youtu.be/Vea4cfn6TOA This is still only a low level mechanistic type of operation, but at least a glimpse into how transformers are operating at inference time.