Text Classifications Learned from Language Model Hidden Layers

Advancements in machine learning methods have yielded powerful natural language generation models. However, in general, these models have drawn concern for being both uninterpretable and uncontrollable. Model interpretability and control have become important topics of interest among researchers. We explore a variety of machine learning methods to classify the hidden states of language models. This classification enables model interpretation at a deep semantic level and is a necessary part of recently proposed model control methods. We show further that the use of language model hidden layers as text representations in classification tasks may be more reliable in some applications than more standard text representations.