Papers by Lekang Jiang
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. |
Enriching Patent Claim Generation with European Patent Dataset (2025.findings-emnlp)
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| Challenge: | Existing work on large language models to assist inventors in writing patent claims relies on datasets from the United States Patent and Trademark Office. |
| Approach: | They propose a European patent dataset that provides rich textual data and structured metadata to support multiple patent-related tasks. |
| Outcome: | The proposed dataset outperforms existing datasets and GPT-4o in claim quality and cross-domain generalization. |
Reasoning for Hierarchical Text Classification: The Case of Patents (2026.findings-acl)
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| Challenge: | Hierarchical text classification (HTC) is one of the hardest HTC scenarios because of professional difficulties and extensive labels. |
| Approach: | They propose a framework that reformulates hierarchical classification as a step-by-step reasoning task. |
| Outcome: | The proposed framework outperforms supervised fine-tuning benchmarks on other widely used HTC benchmarks. |