@inproceedings{yan-etal-2026-t2er,
title = "{T}2{ER}: A {C}hinese Benchmark for Text-to-Executable-Rule Generation in Medical Insurance",
author = "Yan, Hanjing and
Li, Rubin and
Lin, Yuxuan and
Zhu, Siyi and
Wang, Yuxiang and
Yu, Guangya and
Chu, Yuxiang and
Fan, Yongqi and
Wu, Yinan and
Wei, Bao and
Zhang, Weiyan and
Ruan, Tong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2165/",
pages = "49246--49264",
ISBN = "979-8-89176-459-0",
abstract = "Medical insurance plays a pivotal role in the healthcare system, where effective supervision relies on the precise execution of complex medical insurance rules. Existing solutions for medical insurance violation detection mainly rely on costly manual labor, limiting efficiency. Although current Large Language Models (LLMs) have demonstrated significant potential in general Natural Language Processing (NLP) tasks, they still struggle to understand these rules, and a corresponding evaluation benchmark is lacking. Hence, we propose T2ER, a benchmark to evaluate the capability of LLMs in converting texts into executable rules in medical insurance. T2ER consists of 2,115 rules and 5,636 synthetic Electronic Medical Record (EMR) test cases for execution verification. Furthermore, we design a dual evaluation framework spanning rule and execution levels to comprehensively assess transformation quality. We conduct extensive experiments on advanced LLMs. The results reveal a significant human-AI performance gap, indicating that this conversion remains a formidable challenge. The dataset is available at https://github.com/YHJ1223/T2ER."
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<abstract>Medical insurance plays a pivotal role in the healthcare system, where effective supervision relies on the precise execution of complex medical insurance rules. Existing solutions for medical insurance violation detection mainly rely on costly manual labor, limiting efficiency. Although current Large Language Models (LLMs) have demonstrated significant potential in general Natural Language Processing (NLP) tasks, they still struggle to understand these rules, and a corresponding evaluation benchmark is lacking. Hence, we propose T2ER, a benchmark to evaluate the capability of LLMs in converting texts into executable rules in medical insurance. T2ER consists of 2,115 rules and 5,636 synthetic Electronic Medical Record (EMR) test cases for execution verification. Furthermore, we design a dual evaluation framework spanning rule and execution levels to comprehensively assess transformation quality. We conduct extensive experiments on advanced LLMs. The results reveal a significant human-AI performance gap, indicating that this conversion remains a formidable challenge. The dataset is available at https://github.com/YHJ1223/T2ER.</abstract>
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%0 Conference Proceedings
%T T2ER: A Chinese Benchmark for Text-to-Executable-Rule Generation in Medical Insurance
%A Yan, Hanjing
%A Li, Rubin
%A Lin, Yuxuan
%A Zhu, Siyi
%A Wang, Yuxiang
%A Yu, Guangya
%A Chu, Yuxiang
%A Fan, Yongqi
%A Wu, Yinan
%A Wei, Bao
%A Zhang, Weiyan
%A Ruan, Tong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yan-etal-2026-t2er
%X Medical insurance plays a pivotal role in the healthcare system, where effective supervision relies on the precise execution of complex medical insurance rules. Existing solutions for medical insurance violation detection mainly rely on costly manual labor, limiting efficiency. Although current Large Language Models (LLMs) have demonstrated significant potential in general Natural Language Processing (NLP) tasks, they still struggle to understand these rules, and a corresponding evaluation benchmark is lacking. Hence, we propose T2ER, a benchmark to evaluate the capability of LLMs in converting texts into executable rules in medical insurance. T2ER consists of 2,115 rules and 5,636 synthetic Electronic Medical Record (EMR) test cases for execution verification. Furthermore, we design a dual evaluation framework spanning rule and execution levels to comprehensively assess transformation quality. We conduct extensive experiments on advanced LLMs. The results reveal a significant human-AI performance gap, indicating that this conversion remains a formidable challenge. The dataset is available at https://github.com/YHJ1223/T2ER.
%U https://aclanthology.org/2026.emnlp-main.2165/
%P 49246-49264
Markdown (Informal)
[T2ER: A Chinese Benchmark for Text-to-Executable-Rule Generation in Medical Insurance](https://aclanthology.org/2026.emnlp-main.2165/) (Yan et al., EMNLP 2026)
ACL
- Hanjing Yan, Rubin Li, Yuxuan Lin, Siyi Zhu, Yuxiang Wang, Guangya Yu, Yuxiang Chu, Yongqi Fan, Yinan Wu, Bao Wei, Weiyan Zhang, and Tong Ruan. 2026. T2ER: A Chinese Benchmark for Text-to-Executable-Rule Generation in Medical Insurance. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 49246–49264, Budapest, Hungary. Association for Computational Linguistics.