The Effectiveness of Bidirectional Generative Patent Language Models
This work addresses the incremental improvement of patent writing efficiency for professionals by enhancing autocomplete functionality in a domain-specific context.
The paper tackles the problem of measuring and improving the effectiveness of generative patent language models for assisting human writing, proposing a simplified autocomplete design that increases effectiveness by over 10% to reach more than 60% keystroke savings. It introduces bidirectional generative models that maintain similar autocomplete effectiveness regardless of where writing starts, enabling consistent assistance throughout the text.
Generative patent language models can assist humans to write patent text more effectively. The question is how to measure effectiveness from a human-centric perspective and how to improve effectiveness. In this manuscript, a simplified design of the autocomplete function is proposed to increase effectiveness by more than 10%. With the new design, the effectiveness of autocomplete can reach more than 60%, which means that more than 60% of keystrokes can be saved by autocomplete. Since writing patent text does not necessarily start from the beginning to the end, a question is whether the generative model can assist a user no matter where to start writing. To answer the question, the generative models in this manuscript are pre-trained with training data in both directions. The generative models become bidirectional. Since text generation is bidirectional, the calculation of autocomplete effectiveness can be bidirectional and starts from anywhere in the text. After thorough experiments, a key finding is that the autocomplete effectiveness of a model for the same text remains similar no matter where the calculation starts. The finding indicates that such bidirectional models can assist a user at a similar level, no matter where the user starts to write.