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Latent Topology Induction for Understanding Contextualized Representations
[article]
2022
arXiv
pre-print
In this work, we study the representation space of contextualized embeddings and gain insight into the hidden topology of large language models. We show there exists a network of latent states that summarize linguistic properties of contextualized representations. Instead of seeking alignments to existing well-defined annotations, we infer this latent network in a fully unsupervised way using a structured variational autoencoder. The induced states not only serve as anchors that mark the
arXiv:2206.01512v1
fatcat:vzefar3urrdt5gyv7mezpcdhve