Transformers need glasses! Information over-squashing in language tasks
<|reference_start|>Transformers need glasses! Information over-squashing in language tasks: We study how information propagates in decoder-only Transformers, which are the architectural backbone of most existing frontier large language models (LLMs). We rely on a theoretical signal propagation analysis -- specifically, we analyse the representations of the last token in the final layer of the Transformer, as this is the representation used for next-token prediction. Our analysis reveals a representational collapse phenomenon: we prove that certain distinct sequences of inputs to the Transformer can yield arbitrarily close representations in the final token. This effect is exacerbated by the low-precision floating-point formats frequently used in modern LLMs. As a result, the model is provably unable to respond to these sequences in different ways -- leading to errors in, e.g., tasks involving counting or copying. Further, we show that decoder-only Transformer language models can lose sensitivity to specific tokens in the input, which relates to the well-known phenomenon of over-squashing in graph neural networks. We provide empirical evidence supporting our claims on contemporary LLMs. Our theory also points to simple solutions towards ameliorating these issues.<|reference_end|>
@article{barbero2024transformers,
    title={Transformers need glasses! Information over-squashing in language tasks},
    author={Federico Barbero, Andrea Banino, Steven Kapturowski, Dharshan Kumaran,
  Jo~ao G.M. Ara'ujo, Alex Vitvitskyi, Razvan Pascanu, Petar Veliv{c}kovi'c},
    journal={arXiv preprint arXiv:2406.04267},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04267},
    primaryClass={cs.CL cs.LG}
}