LegalRikai: Open Benchmark – a Benchmark for Complex Japanese Corporate Legal Tasks

Shogo Fujita, Yuji Naraki, Yiqing Zhu, Shinsuke Mori


Abstract
This paper introduces LegalRikai: Open Benchmark, a new benchmark comprising four complex tasks that emulate Japanese corporate legal practices. The benchmark was created by legal professionals under the supervision of an attorney. This benchmark has 100 samples that require long-form, structured outputs, and we evaluated them against multiple practical criteria. We conducted both human and automated evaluations using leading LLMs, including GPT-5, Gemini 2.5 Pro, and Claude Opus 4.1. Our human evaluation revealed that abstract instructions prompted unnecessary modifications, highlighting model weaknesses in document-level editing that were missed by conventional short-text tasks. Furthermore, our analysis reveals that automated evaluation aligns well with human judgment on criteria with clear linguistic grounding, and assessing structural consistency remains a challenge. The result demonstrates the utility of automated evaluation as a screening tool when expert availability is limited. We propose a dataset evaluation framework to promote more practice-oriented research in the legal domain.
Anthology ID:
2026.lrec-main.397
Volume:
Proceedings of the Fifteenth Language Resources and Evaluation Conference
Month:
May
Year:
2026
Address:
Palma de Mallorca, Spain
Editors:
Stelios Piperidis, Núria Bel, Henk van den Heuvel, Nancy Ide, Simon Krek, Antonio Toral
Venue:
LREC
SIG:
Publisher:
ELRA Language Resource Association
Note:
Pages:
5055–5077
Language:
URL:
https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.397/
DOI:
Bibkey:
Cite (ACL):
Shogo Fujita, Yuji Naraki, Yiqing Zhu, and Shinsuke Mori. 2026. LegalRikai: Open Benchmark – a Benchmark for Complex Japanese Corporate Legal Tasks. International Conference on Language Resources and Evaluation, main:5055–5077.
Cite (Informal):
LegalRikai: Open Benchmark – a Benchmark for Complex Japanese Corporate Legal Tasks (Fujita et al., LREC 2026)
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PDF:
https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.397.pdf