Summary of Patentgpt: a Large Language Model For Patent Drafting Using Knowledge-based Fine-tuning Method, by Runtao Ren et al.
PatentGPT: A Large Language Model for Patent Drafting Using Knowledge-based Fine-tuning Method
by Runtao Ren, Jian Ma
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel framework called Knowledge Fine-Tuning (KFT) that enables large language models (LLMs) to generate patent documents accurately. The proposed approach, PatentGPT, combines knowledge graph-based pre-training with domain-specific supervised fine-tuning and reinforcement learning from human feedback. Compared to state-of-the-art models, PatentGPT shows exceptional performance in patent-related benchmark tests, achieving scores up to 400% higher. This breakthrough has the potential to revolutionize intellectual property generation by augmenting human creativity and innovation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how artificial intelligence can help with creating new ideas for inventions. Right now, it’s hard for AI models to understand complex technical concepts needed to create patent documents. The researchers propose a new way to fine-tune these models so they can better understand domain-specific knowledge and generate more accurate patent documents. Their approach, called PatentGPT, shows great promise in creating high-quality patents that can help inventors get their ideas protected. |
Keywords
» Artificial intelligence » Fine tuning » Knowledge graph » Reinforcement learning from human feedback » Supervised
