Summary of Autopatent: a Multi-agent Framework For Automatic Patent Generation, by Qiyao Wang et al.
AutoPatent: A Multi-Agent Framework for Automatic Patent Generation
by Qiyao Wang, Shiwen Ni, Huaren Liu, Shule Lu, Guhong Chen, Xi Feng, Chi Wei, Qiang Qu, Hamid Alinejad-Rokny, Yuan Lin, Min Yang
First submitted to arxiv on: 13 Dec 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 The paper introduces a novel task, Draft2Patent, which challenges Large Language Models (LLMs) to generate full-length patents based on initial drafts. The task is more complex than previous tasks in patent processing, such as classification and short text generation, due to the specialized nature of patents, standardized terminology, and extensive length. To address this challenge, the paper proposes a multi-agent framework called AutoPatent, which leverages LLM-based planner, writer, and examiner agents with PGTree and RRAG to generate high-quality patent documents. The experimental results show that AutoPatent significantly enhances the ability to generate comprehensive patents across various LLMs, and even outperforms larger models in both objective metrics and human evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way for computers to help with writing patents. Right now, there are big language models that can write short texts, but they struggle with longer, more complicated documents like patents. The researchers came up with a new task called Draft2Patent, where these models have to take an initial draft and turn it into a full patent document. They also created a special system called AutoPatent that uses different parts to help the model write better patents. The results show that their system is really good at writing patents and even does better than bigger and more powerful language models. |
Keywords
» Artificial intelligence » Classification » Text generation
