@inproceedings{hu-etal-2026-taxpraben,
title = "{T}ax{P}ra{B}en: A Scalable Benchmark for Structured Evaluation of {LLM}s in {C}hinese Real-World Tax Practice",
author = "Hu, Gang and
Chen, Yating and
Ding, Haiyan and
Gao, Wang and
Jiajia, Huang and
Peng, Min and
Xie, Qianqian and
Yue, Kun",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1765/",
pages = "38061--38104",
ISBN = "979-8-89176-390-6",
abstract = "While Large Language Models (LLMs) excel in various general domains, they exhibit notable gaps in the highly specialized, knowledge-intensive, and legally regulated Chinese tax domain. Consequently, while tax-related benchmarks are gaining attention, many focus on isolated NLP tasks, neglecting real-world practical capabilities. To address this issue, we introduce TaxPraBen, the first dedicated benchmark for Chinese taxation practice. It combines 10 traditional application tasks, along with 3 pioneering real-world scenarios: tax risk prevention, tax inspection analysis, and tax strategy planning, sourced from 14 datasets totaling 7.3K instances. TaxPraBen features a scalable structured evaluation paradigm designed through process of ``structured parsing{---}field alignment extraction{---}numerical and textual matching'', enabling end-to-end tax practice assessment while being extensible to other domains. We evaluate 19 LLMs based on Bloom{'}s taxonomy. The results indicate significant performance disparities: all closed-source large-parameter LLMs excel, and Chinese LLMs like Qwen2.5 generally exceed multilingual LLMs, while the YaYi2 LLM, fine-tuned with some tax data, shows only limited improvement. TaxPraBen[{\ensuremath{<}}https://anonymous.4open.science/r/TaxPraBen/{\ensuremath{>}}] serves as a vital resource for advancing evaluations of LLMs in practical applications."
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<abstract>While Large Language Models (LLMs) excel in various general domains, they exhibit notable gaps in the highly specialized, knowledge-intensive, and legally regulated Chinese tax domain. Consequently, while tax-related benchmarks are gaining attention, many focus on isolated NLP tasks, neglecting real-world practical capabilities. To address this issue, we introduce TaxPraBen, the first dedicated benchmark for Chinese taxation practice. It combines 10 traditional application tasks, along with 3 pioneering real-world scenarios: tax risk prevention, tax inspection analysis, and tax strategy planning, sourced from 14 datasets totaling 7.3K instances. TaxPraBen features a scalable structured evaluation paradigm designed through process of “structured parsing—field alignment extraction—numerical and textual matching”, enabling end-to-end tax practice assessment while being extensible to other domains. We evaluate 19 LLMs based on Bloom’s taxonomy. The results indicate significant performance disparities: all closed-source large-parameter LLMs excel, and Chinese LLMs like Qwen2.5 generally exceed multilingual LLMs, while the YaYi2 LLM, fine-tuned with some tax data, shows only limited improvement. TaxPraBen[\ensuremath<https://anonymous.4open.science/r/TaxPraBen/\ensuremath>] serves as a vital resource for advancing evaluations of LLMs in practical applications.</abstract>
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%0 Conference Proceedings
%T TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice
%A Hu, Gang
%A Chen, Yating
%A Ding, Haiyan
%A Gao, Wang
%A Jiajia, Huang
%A Peng, Min
%A Xie, Qianqian
%A Yue, Kun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F hu-etal-2026-taxpraben
%X While Large Language Models (LLMs) excel in various general domains, they exhibit notable gaps in the highly specialized, knowledge-intensive, and legally regulated Chinese tax domain. Consequently, while tax-related benchmarks are gaining attention, many focus on isolated NLP tasks, neglecting real-world practical capabilities. To address this issue, we introduce TaxPraBen, the first dedicated benchmark for Chinese taxation practice. It combines 10 traditional application tasks, along with 3 pioneering real-world scenarios: tax risk prevention, tax inspection analysis, and tax strategy planning, sourced from 14 datasets totaling 7.3K instances. TaxPraBen features a scalable structured evaluation paradigm designed through process of “structured parsing—field alignment extraction—numerical and textual matching”, enabling end-to-end tax practice assessment while being extensible to other domains. We evaluate 19 LLMs based on Bloom’s taxonomy. The results indicate significant performance disparities: all closed-source large-parameter LLMs excel, and Chinese LLMs like Qwen2.5 generally exceed multilingual LLMs, while the YaYi2 LLM, fine-tuned with some tax data, shows only limited improvement. TaxPraBen[\ensuremath<https://anonymous.4open.science/r/TaxPraBen/\ensuremath>] serves as a vital resource for advancing evaluations of LLMs in practical applications.
%U https://aclanthology.org/2026.acl-long.1765/
%P 38061-38104
Markdown (Informal)
[TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice](https://aclanthology.org/2026.acl-long.1765/) (Hu et al., ACL 2026)
ACL
- Gang Hu, Yating Chen, Haiyan Ding, Wang Gao, Huang Jiajia, Min Peng, Qianqian Xie, and Kun Yue. 2026. TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 38061–38104, San Diego, California, United States. Association for Computational Linguistics.