Summary of Patenteval: Understanding Errors in Patent Generation, by You Zuo (almanach) et al.
PatentEval: Understanding Errors in Patent Generation
by You Zuo, Kim Gerdes, Eric Villemonte de La Clergerie, Benoît Sagot
First submitted to arxiv on: 5 Jun 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 comprehensive error typology introduced in this work focuses on evaluating two crucial tasks in machine-generated patent texts: claims-to-abstract generation and generating the next claim given previous ones. A benchmark, PatentEval, is developed to assess language models systematically in this context. The study presents a comparative analysis of various models, including those trained for specific patent-related tasks and general-purpose large language models (LLMs). Additionally, metrics are explored and evaluated to approximate human judgments in patent text evaluation, examining the alignment with expert assessments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Patent text generation is important, but current language models struggle. Researchers developed a new way to measure how well these models do their job by creating a list of errors they might make. They tested many different types of models and found that some are better than others at generating patent texts. The study also looked at ways to improve measuring how good the models are, by seeing if they match what experts think. |
Keywords
» Artificial intelligence » Alignment » Text generation
