@inproceedings{cao-etal-2026-jbe,
title = "{JBE}-{QA}: {J}apanese Bar Exam {QA} Dataset for Assessing Legal Domain Knowledge",
author = "Cao, Zhihan and
Nishino, Fumihito and
Yamada, Hiroaki and
Nguyen, Ha Thanh and
Miyao, Yusuke and
Satoh, Ken",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.415/",
doi = "10.63317/5knc9vm8vgda",
pages = "5317--5327",
abstract = "We introduce JBE-QA, a Japanese Bar Exam Question{--}Answering dataset to evaluate large language models' legal knowledge. Derived from the multiple-choice (tant{\={o}}-shiki) section of the Japanese bar exam (2015{--}2024), JBE-QA provides the first comprehensive benchmark for Japanese legal-domain evaluation of LLMs. It covers the Civil Code, the Penal Code, and the Constitution, extending beyond the Civil Code focus of prior Japanese resources. Each question is decomposed into independent true/false judgments with structured contextual fields. The dataset contains 3,464 items with balanced labels. We evaluate 26 LLMs, including proprietary, open-weight, Japanese-specialised, and reasoning models. Our results show that proprietary models with reasoning enabled perform best, and the Constitution questions are generally easier than the Civil Code or the Penal Code questions."
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<abstract>We introduce JBE-QA, a Japanese Bar Exam Question–Answering dataset to evaluate large language models’ legal knowledge. Derived from the multiple-choice (tantō-shiki) section of the Japanese bar exam (2015–2024), JBE-QA provides the first comprehensive benchmark for Japanese legal-domain evaluation of LLMs. It covers the Civil Code, the Penal Code, and the Constitution, extending beyond the Civil Code focus of prior Japanese resources. Each question is decomposed into independent true/false judgments with structured contextual fields. The dataset contains 3,464 items with balanced labels. We evaluate 26 LLMs, including proprietary, open-weight, Japanese-specialised, and reasoning models. Our results show that proprietary models with reasoning enabled perform best, and the Constitution questions are generally easier than the Civil Code or the Penal Code questions.</abstract>
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%0 Conference Proceedings
%T JBE-QA: Japanese Bar Exam QA Dataset for Assessing Legal Domain Knowledge
%A Cao, Zhihan
%A Nishino, Fumihito
%A Yamada, Hiroaki
%A Nguyen, Ha Thanh
%A Miyao, Yusuke
%A Satoh, Ken
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F cao-etal-2026-jbe
%X We introduce JBE-QA, a Japanese Bar Exam Question–Answering dataset to evaluate large language models’ legal knowledge. Derived from the multiple-choice (tantō-shiki) section of the Japanese bar exam (2015–2024), JBE-QA provides the first comprehensive benchmark for Japanese legal-domain evaluation of LLMs. It covers the Civil Code, the Penal Code, and the Constitution, extending beyond the Civil Code focus of prior Japanese resources. Each question is decomposed into independent true/false judgments with structured contextual fields. The dataset contains 3,464 items with balanced labels. We evaluate 26 LLMs, including proprietary, open-weight, Japanese-specialised, and reasoning models. Our results show that proprietary models with reasoning enabled perform best, and the Constitution questions are generally easier than the Civil Code or the Penal Code questions.
%R 10.63317/5knc9vm8vgda
%U https://aclanthology.org/2026.lrec-1.415/
%U https://doi.org/10.63317/5knc9vm8vgda
%P 5317-5327
Markdown (Informal)
[JBE-QA: Japanese Bar Exam QA Dataset for Assessing Legal Domain Knowledge](https://aclanthology.org/2026.lrec-1.415/) (Cao et al., LREC 2026)
ACL