Efficient and scalable masked word prediction using concept formation

This paper introduces Cobweb/4L, a novel approach for efficient language model learning that supports masked word prediction. The approach builds on Cobweb, an incremental system that learns a hierarchy of probabilistic concepts. Each concept stores the frequencies of words that appear in instances tagged with the concept label. The system utilizes an attribute-value representation to encode words and their context into instances. Cobweb/4L uses an information-theoretic variant of category utility as well as a new performance mechanism that leverages multiple concepts to generate predictions. We demonstrate that its new performance mechanism substantially outperforms prior Cobweb performance mechanisms that use only a single node to generate predictions. Further, we demonstrate that Cobweb/4L outperforms transformer-based language models in a low-data setting by learning more rapidly and achieving better final performance. Lastly, we show that Cobweb/4L, which is hyperparameter-free, is robust across varying scales of training data and does not require any manual tuning. This is in contrast to Word2Vec, which performs best with a varying number of hidden nodes that depend on the total amount of training data; this means its hyperparameters must be manually tuned for different amounts of training data. We conclude by discussing future directions for Cobweb/4L.