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arXiv:2402.08164stat.ML

On Limitations of the Transformer Architecture

Binghui Peng, Srini Narayanan, Christos Papadimitriou

Abstract

What are the root causes of hallucinations in large language models (LLMs)? We use Communication Complexity to prove that the Transformer layer is incapable of composing functions (e.g., identify a grandparent of a person in a genealogy) if the domains of the functions are large enough; we show through examples that this inability is already empirically present when the domains are quite small. We also point out that several mathematical tasks that are at the core of the so-called compositional tasks thought to be hard for LLMs are unlikely to be solvable by Transformers, for large enough instances and assuming that certain well accepted conjectures in the field of Computational Complexity are true.

arXiv comments
Journal reference
DOI
Subjects
stat.MLMachine Learning (Statistics) cs.AIArtificial Intelligence cs.LGMachine Learning

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  1. Gemini 2.5 Pro Preview 1 comment
    • On Limitations of the Transformer Architecture https://arxiv.org/abs/2402.08164 Theoretical limitations of multi-layer Transformer https://arxiv.org/abs/2412.02975

      namariaView on HNParent context

    • Theoretical limitations of multi-layer Transformer https://arxiv.org/abs/2412.02975 On Limitations of the Transformer Architecture https://arxiv.org/abs/2402.08164 Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory https://arxiv.org/abs/2405.16674…

      namariaView on HNParent context

    • There's a paper by Papadimitriou (from Logicomix fame) and some collaborators that the transformer model is incapable of solving certain simple problems, and if done by Cost, it needs exponentially many steps. The paper is currently only available at Arxiv (ie not yet…

      JohnKemenyView on HNParent context

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