Summary of Pap2pat: Benchmarking Outline-guided Long-text Patent Generation with Patent-paper Pairs, by Valentin Knappich et al.
Pap2Pat: Benchmarking Outline-Guided Long-Text Patent Generation with Patent-Paper Pairs
by Valentin Knappich, Simon Razniewski, Anna Hätty, Annemarie Friedrich
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a new benchmark for patent drafting, called PAP2PAT, which consists of 1.8k patent-paper pairs describing the same inventions. To address the complex task of long-document patent generation, the authors suggest a chunk-based outline-guided generation method that leverages information from research papers serving as invention specifications. The evaluation shows that Large Language Models (LLMs) can effectively use paper information but still struggle to provide sufficient detail. Fine-tuning leads to more patent-style language, but also increases hallucination. The authors release their data and code. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are struggling with long technical texts. Patent drafting is a big challenge because patents have very detailed descriptions. Usually, these descriptions are written by experts who know the technology inside out. But can computers help with this task? The answer is yes, but only to some extent. Computers can use information from research papers that describe inventions, but they still lack the level of detail required for patent drafting. This makes it important to develop better methods and tools for computers to assist in patent drafting. |
Keywords
» Artificial intelligence » Fine tuning » Hallucination
