Unpacking Transformer Models: Autoregressivity and Probabilistic Expressivity

Transformers, a cornerstone in natural language processing, have typically been studied as language recognizers, machines that accept or reject strings. However, in practical scenarios, they're more often deployed as language models, generating strings in an autoregressive and probabilistic manner. This distinction is more than academic. it has real-world implications for what these models can do.

Autoregressivity: A Boost in Expressivity

When transformers are configured to be autoregressive, their expressivity can increase. What does that mean for the real world? In simple terms, autoregressive models predict the next item in a sequence based on previously seen data. This capability can enhance tasks like text completion and machine translation. The paper, published in Japanese, reveals that autoregressivity can sometimes push the boundaries of what these models can express, providing a more nuanced understanding of language structures.

Probabilistic Models: Breaking Traditional Equivalences

Introducing probabilistic elements to transformers breaks certain equivalences that exist in non-probabilistic scenarios. Consider this: in traditional language recognition, a string is either accepted or rejected. However, when viewed probabilistically, the model assigns probabilities to sequences, enabling a more flexible and expressive output. This shift in approach can revolutionize fields like speech recognition, where outputs aren't just binary but graded.

Why Should We Care?

The benchmark results speak for themselves. By embracing autoregressive and probabilistic methods, transformers can offer more sophisticated language modeling capabilities. But here's the real question: Are we underestimating the potential of these models by sticking to old definitions? Western coverage has largely overlooked this nuanced view of transformers' capabilities. It's high time the spotlight shifts, acknowledging the potential lying in these methods.

This research not only redefines our understanding but also nudges the industry toward more advanced applications. As AI systems become increasingly integrated into daily life, understanding their capabilities isn't just interesting, it's imperative.