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The computational complexity of the self-attention mechanism in Transformermodels significantly limits their ability to generalize over long temporaldurations. Memory-augmentation, or the explicit storing of past information inexternal memory for subsequent predictions, has become a constructive avenuefor mitigating this limitation. We argue that memory-augmented Transformers canbenefit substantially from considering insights from the memory literature inhumans. We detail an approach for integrating evidence from the human memorysystem through the specification of cross-domain linking hypotheses. We thenprovide an empirical demonstration to evaluate the use of surprisal as alinking hypothesis, and further identify the limitations of this approach toinform future research.

Memory in humans and deep language models: Linking hypotheses for model augmentation