Papers by Pascal A. Scherz
Towards Better Evaluation for Generated Patent Claims (2025.acl-long)
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| Challenge: | Existing studies highlight inconsistencies between automated evaluation metrics and human expert assessments for patent claims. |
| Approach: | They propose a multi-dimensional evaluation method specifically designed for patent claims that incorporates features annotated by patent experts. |
| Outcome: | The proposed method achieves highest correlation with human expert evaluations across all assessment criteria across all tested metrics. |
Can Large Language Models Generate High-quality Patent Claims? (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) have shown exceptional performance across various text generation tasks, but remain under-explored in the patent domain, which offers highly structured and precise language. |
| Approach: | They construct a dataset to investigate the performance of current LLMs in patent claim generation. |
| Outcome: | The proposed model outperforms state-of-the-art general LLMs in patent claim generation. |
Patent-CR: A Dataset for Patent Claim Revision (2025.naacl-long)
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| Challenge: | Patent-CR is the first dataset created for the patent claim revision task in English. |
| Approach: | They propose to create a dataset for the patent claim revision task in English that includes both initial patent applications rejected by examiners and the final granted versions. |
| Outcome: | The proposed dataset includes both initial patent applications rejected by examiners and the final granted versions. |