Your Context Is Not an Array: Unveiling Random Access Limitations in Transformers

M.Reza Ebrahimi, Sunny Panchal, Roland Memisevic

Your Context Is Not an Array: Unveiling Random Access Limitations in Transformers: 9 upvotes on Hugging Face Daily Papers, #11 of 11 papers on 2024-08-13. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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