The Effectiveness of Bidirectional Generative Patent Language Models

TL;DR

This paper introduces bidirectional generative patent language models that significantly improve autocomplete effectiveness, enabling more efficient patent writing regardless of where the user begins, by leveraging bidirectional training.

Contribution

The paper proposes a novel bidirectional training approach for patent language models, enhancing autocomplete effectiveness and usability across different starting points in text.

Findings

Autocomplete effectiveness exceeds 60%.

Effectiveness remains consistent regardless of starting position.

Bidirectional models assist users uniformly across text segments.

Abstract

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.