19 citations · 45 across the 6 of their papers we have counts for
6 papers · 1 filter
InstructPatentGPT: Training patent language models to follow instructions with human feedback
Jieh-Sheng Lee
In this research, patent prosecution is conceptualized as a system of reinforcement learning from human feedback. The objective of the system is to increase the likelihood for a la…
LexGPT 0.1: pre-trained GPT-J models with Pile of Law
Jieh-Sheng Lee
This research aims to build generative language models specialized for the legal domain. The manuscript presents the development of LexGPT models based on GPT-J models and pre-trai…
The Effectiveness of Bidirectional Generative Patent Language Models
Jieh-Sheng Lee
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…
Evaluating Generative Patent Language Models
Jieh-Sheng Lee
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…
PatentTransformer-2: Controlling Patent Text Generation by Structural Metadata
Jieh-Sheng Lee, Jieh Hsiang
PatentTransformer is our codename for patent text generation based on Transformer-based models. Our goal is "Augmented Inventing." In this second version, we leverage more of the s…
Personalized Patent Claim Generation and Measurement
Jieh-Sheng Lee
This work-in-progress paper proposes a framework to generate and measure personalized patent claims. The objective is to help inventors conceive better inventions by learning from…