arXiv:2602.18333v2 Announce Type: replace Abstract: Despite the remarkable practical success of transformer-based language models, recent work has raised concerns about their ability to perform state tracking. In particular, a growing body of literature has shown this limitation primarily through failures in out-of-distribution (OOD) generalization, such as length extrapolation. In this work, we shift attention to the in-distribution implications of these limitations. We conduct a large-scale experimental study of the data efficiency of transformers and recurrent neural networks (RNNs) across
The paper identifies fundamental limitations of transformer-based LLMs concerning state tracking and generalization, a timely and critical area of AI research as these models become more pervasive.
Understanding the in-distribution limitations of current LLMs is crucial for developers and deployers to prevent catastrophic failures in real-world applications and to guide the next generation of AI architectures.
The focus shifts from solely out-of-distribution generalization failures to the 'in-distribution' implications, potentially leading to more robust model evaluation and a re-evaluation of transformer supremacy for certain tasks.
- · Researchers in recurrent neural networks
- · AI safety and alignment researchers
- · Developers of specialized AI architectures
- · Companies over-reliant on simple transformer scaling
- · Practitioners ignoring model limitations
- · The 'bigger is always better' paradigm
Increased scrutiny and demand for more robust benchmarks for current large language models.
Renewed investment and research into alternative or hybrid AI architectures that address state-tracking limitations.
A potential slowing of some AI deployment in critical sectors until more reliable models become available.
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Read at arXiv cs.LG