PatentEval: Understanding Errors in Patent Generation
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916299690475520 |
|---|---|
| author | Zuo, You Gerdes, Kim de La Clergerie, Eric Villemonte Sagot, Benoît |
| author_facet | Zuo, You Gerdes, Kim de La Clergerie, Eric Villemonte Sagot, Benoît |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06589 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PatentEval: Understanding Errors in Patent Generation Zuo, You Gerdes, Kim de La Clergerie, Eric Villemonte Sagot, Benoît Computation and Language Artificial Intelligence 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. |
| title | PatentEval: Understanding Errors in Patent Generation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.06589 |