Exploring Hierarchical Structure Representation in Transformer Models through Computational Mechanics

Olli Järviniemi, Udayanto Dwi Atmojo, Aayush Kucheria, Konsta Tiilikainen

This work attempts to explore how an approach based on computational mechanics can cope when a more complex hierarchical generative process is involved, i.e, a process that comprises Hidden Markov Models (HMMs) whose transition probabilities change over time.

We find that small transformer models are capable of modeling such changes in an HMM. However, our preliminary investigations did not find geometrically represented probabilities for different hypotheses.

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Cite this work

@misc {

title={

@misc {

},

author={

Olli Järviniemi, Udayanto Dwi Atmojo, Aayush Kucheria, Konsta Tiilikainen

},

date={

6/2/24

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.