| Challenge: | a patent is a legal instrument that grants inventors or entities exclusive rights over their invention for a designated period. |
| Approach: | They propose a typology specifically designed for evaluating two distinct tasks in machine-generated patent texts. |
| Outcome: | The proposed approach provides valuable insights into the capabilities and limitations of current language models in the specialized field of patent text generation. |
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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. |
Patentformer: A Novel Method to Automate the Generation of Patent Applications (2024.emnlp-industry)
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| Challenge: | Patentformer is a novel method for generating patent specification by fine-tuning the generative models with diverse sources of information, e.g., patent claims, drawing text, and brief descriptions of the drawings. |
| Approach: | They propose a method for generating patent specification by fine-tuning generative models with diverse sources of information, e.g., patent claims, drawing text, and brief descriptions of the drawings. |
| Outcome: | The proposed method generates patent specification in legal writing style and human-like quality may be better than the actual specification. |
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. |
PatentScore: Multi-dimensional Evaluation of LLM-Generated Patent Claims (2025.emnlp-main)
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| Challenge: | Existing natural language generation (NLG) metrics fail to capture domain-specific nuances . patent claims require precise assessment of structural elements such as antecedent consistency and claim dependency. |
| Approach: | They propose a multi-dimensional evaluation framework specifically designed for patent claims . PatentScore integrates hierarchical decomposition of claim elements, validation patterns and scoring across structural, semantic, and legal dimensions. |
| Outcome: | The proposed evaluation framework outperforms existing evaluation frameworks on patent claims . patentScore achieved highest correlation with expert annotations on 400 patent claims dataset . |
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. |
Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Models (2026.acl-long)
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| Challenge: | Recent advances in large language models have raised concerns about reliability and trustworthiness of the models. |
| Approach: | They analyze 134 papers and introduce a taxonomy of evidence-based text generation with LLMs. |
| Outcome: | The proposed methods highlight open challenges and outline promising directions for future work. |
Curious Case of Language Generation Evaluation Metrics: A Cautionary Tale (2020.coling-main)
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| Challenge: | a few popular metrics are still used to evaluate language generation systems despite their known limitations. |
| Approach: | They propose to use automatic metrics to evaluate language generation systems . they show that they prefer system outputs to human-authored texts . |
| Outcome: | The proposed metrics are insensitive to correct translations of rare words and can yield high scores when given a single sentence as system output for the entire test set. |
A Survey on Patent Analysis: From NLP to Multimodal AI (2025.acl-long)
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| Challenge: | Recent advances in pretrained language models and large language models have demonstrated transformative capabilities across diverse domains. |
| Approach: | They propose a taxonomy for categorization based on tasks in the patent life cycle . they introduce a novel taxonomies for categorizing based upon tasks in patent life cycles . |
| Outcome: | The proposed method is based on tasks in the patent life cycle and provides a taxonomy for categorization based upon tasks in patent life cycles. |
TESTEVAL: Benchmarking Large Language Models for Test Case Generation (2025.findings-naacl)
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Wenhan Wang, Chenyuan Yang, Zhijie Wang, Yuheng Huang, Zhaoyang Chu, Da Song, Lingming Zhang, An Ran Chen, Lei Ma
| Challenge: | Existing methods to generate test cases using large language models are limited in their ability to generate unit test cases. |
| Approach: | They propose a test case generation benchmark that uses large language models to generate unit test cases. |
| Outcome: | The proposed test case generation benchmarks compare LLMs with commercial and open-source LLM platforms and find that they lack the ability to comprehend program logic and execution paths. |