Your Context Is Not an Array: Unveiling Random Access Limitations in
  Transformers
Authors: name: MohammadReza Ebrahimi, name: Sunny Panchal, name: Roland Memisevic

Despite their recent successes, Transformer-based large language models show
surprising failure modes. A well-known example of such failure modes is their
inability to length-generalize: solving problem instances at inference time
that are longer than those seen during training. In this work, we further
explore the root cause of this failure by performing a detailed analysis of
model behaviors on the simple parity task. Our analysis suggests that length
generalization failures are intricately related to a model's inability to
perform random memory accesses within its context window. We present supporting
evidence for this hypothesis by demonstrating the effectiveness of
methodologies that circumvent the need for indexing or that enable random token
access indirectly, through content-based addressing. We further show where and
how the failure to perform random memory access manifests through attention map
visualizations.