arXiv:2606.08768v1 Announce Type: new Abstract: Transformers consistently fail to learn certain simple functions that are provably expressible with specific parameter settings. This gap between learnability and expressivity is particularly prominent for sensitive functions -- functions whose output is likely to change if a single bit of the input is flipped -- for example, PARITY. While prior work has established that transformers exhibit a bias toward functions with low average sensitivity, the precise mechanism underlying this bias remains poorly understood. To shed light on this phenomenon,
The paper investigates a known limitation of Transformers related to their inability to learn certain simple functions, building on prior work identifying a bias towards low average sensitivity functions.
Understanding the fundamental limitations and biases of Transformer architectures is crucial for their continued development and deployment in critical AI applications, impacting future research directions and practical implementations.
This research provides deeper insight into the 'learnability vs. expressivity' gap in Transformers, potentially leading to the development of more robust and reliable AI models capable of handling a wider range of computational tasks.
- · AI researchers
- · Deep learning framework developers
- · AI safety and interpretability initiatives
- · Developers relying solely on current Transformer architectures for sensitive fun
- · Companies with AI models exhibiting these specific biases
Improved theoretical understanding of Transformer capabilities and limitations emerges.
New architectural modifications or training methodologies are developed to mitigate identified biases.
These improvements lead to more generalizable and trustworthy AI systems across various domains.
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Read at arXiv cs.LG