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Insights into the Linearity of Relation Decoding in Transformer Language Models
Transformer language models process inputs by transforming representations of sentences through math operations. Researchers found that in many cases, the math operations in transformers can be approximated by a simple linear transformation, such as linear or logistic regression, at an early layer of the neural network. The meaningful information in these models is encoded in the MLP layers rather than the attention mechanism. By the halfway point of the network, the subject often contains all the necessary information to predict a fact. This insight provides a better understanding of where the representations in the neural network become useful and highlights the transition from loading context to making predictions in the language models.
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