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.

翻译：生成式语言模型有望在多个领域辅助人类写作。本文旨在构建专利领域的生成式语言模型，并从以人为中心的角度评估模型性能。该视角通过基于生成式专利语言模型的自动补全功能所节省的击键次数比例来衡量。比例越高表明模型越有效，能节省更多击键操作。该指标可用于模型性能基准测试，其与传统以机器为中心的指标不同——后者基于词元而非击键。在模型规模方面，本文构建的最大模型参数量达6B，为专利领域当前最先进水平。基于该指标发现，最大规模的模型并非在以人为中心的指标上表现最优。这一发现表明，若仅以自动补全辅助人类写作为目标，持续扩大专利领域的模型规模可能并不必要。本研究从头预训练了多个专利语言模型，现已公开发布供后续研究者使用，同时提供了若干可视化工具。在专利领域构建生成式语言模型的重要意义在于其未来可能促进创造力与创新。