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        <title>PatentEval: Understanding Errors in Patent Generation</title>
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    <abstract>In this work, we introduce a comprehensive error typology specifically designed for evaluating two distinct tasks in machine-generated patent texts: claims-to-abstract generation, and the generation of the next claim given previous ones. We have also developed a benchmark, PatentEval, for systematically assessing language models in this context. Our study includes a comparative analysis, annotated by humans, of various models. These range from those specifically adapted during training for tasks within the patent domain to the latest general-purpose large language models (LLMs). Furthermore, we explored and evaluated some metrics to approximate human judgments in patent text evaluation, analyzing the extent to which these metrics align with expert assessments. These approaches provide valuable insights into the capabilities and limitations of current language models in the specialized field of patent text generation.</abstract>
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%0 Conference Proceedings
%T PatentEval: Understanding Errors in Patent Generation
%A Zuo, You
%A Gerdes, Kim
%A Clergerie, Éric
%A Sagot, Benoît
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F zuo-etal-2024-patenteval
%X In this work, we introduce a comprehensive error typology specifically designed for evaluating two distinct tasks in machine-generated patent texts: claims-to-abstract generation, and the generation of the next claim given previous ones. We have also developed a benchmark, PatentEval, for systematically assessing language models in this context. Our study includes a comparative analysis, annotated by humans, of various models. These range from those specifically adapted during training for tasks within the patent domain to the latest general-purpose large language models (LLMs). Furthermore, we explored and evaluated some metrics to approximate human judgments in patent text evaluation, analyzing the extent to which these metrics align with expert assessments. These approaches provide valuable insights into the capabilities and limitations of current language models in the specialized field of patent text generation.
%U https://aclanthology.org/2024.naacl-long.147
%P 2687-2710

Markdown (Informal)

[PatentEval: Understanding Errors in Patent Generation](https://aclanthology.org/2024.naacl-long.147) (Zuo et al., NAACL 2024)

• PatentEval: Understanding Errors in Patent Generation (Zuo et al., NAACL 2024)

ACL

• You Zuo, Kim Gerdes, Éric Clergerie, and Benoît Sagot. 2024. PatentEval: Understanding Errors in Patent Generation. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 2687–2710, Mexico City, Mexico. Association for Computational Linguistics.