Gated recurrent neural networks discover attention

Nicolas Zucchet, Seijin Kobayashi, Yassir Akram, Johannes Von Oswald, Maxime Larcher, Angelika Steger, Joao Sacramento

Primary Area: general machine learning (i.e., none of the above)

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Keywords: RNNs, SSMs, LRU, linear self-attention, in-context learning, mechanistic interpretability, multiplicative interactions, gating

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Abstract: Recent architectural developments have enabled recurrent neural networks (RNNs) to reach and even surpass the performance of Transformers on certain sequence modeling tasks. These modern RNNs feature a prominent design pattern: linear recurrent layers interconnected by feedforward paths with multiplicative gating. Here, we show how RNNs equipped with these two design elements can exactly implement (linear) self-attention, the main building block of Transformers. By reverse-engineering a set of trained RNNs, we find that gradient descent in practice discovers our construction. In particular, we examine RNNs trained to solve simple in-context learning tasks on which Transformers are known to excel and find that gradient descent instills in our RNNs the same attention-based in-context learning algorithm used by Transformers. Our findings highlight the importance of multiplicative interactions in neural networks and suggest that certain RNNs might be unexpectedly implementing attention under the hood.

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Submission Number: 5288