  Generative language models are promising for assisting human writing in
various domains. This manuscript aims to build generative language models in
the patent domain and evaluate model performance from a human-centric
perspective. The perspective is to measure the ratio of keystrokes that can be
saved by autocompletion based on generative patent language models. A higher
ratio means a more effective model which can save more keystrokes. This metric
can be used to benchmark model performance. The metric is different from
conventional machine-centric metrics that are token-based instead of
keystroke-based. In terms of model size, the largest model built in this
manuscript is 6B, which is state-of-the-art in the patent domain. Based on the
metric, it is found that the largest model is not necessarily the best for the
human-centric metric. The finding means that keeping increasing model sizes in
the patent domain might be unnecessary if the purpose is to assist human
writing with autocompletion. Several patent language models are pre-trained
from scratch in this research. The pre-trained models are released for future
researchers. Several visualization tools are also provided. The importance of
building a generative language model in the patent domain is the potential to
facilitate creativity and innovations in the future.
