Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models
Yeeun Kim, Youngrok Choi, Eunkyung Choi, JinHwan Choi, Hai Jin Park, Wonseok Hwang
Abstract
Large language models (LLMs) have demonstrated remarkable performance in the legal domain, with GPT-4 even passing the Uniform Bar Exam in the U.S. However their efficacy remains limited for non-standardized tasks and tasks in languages other than English. This underscores the need for careful evaluation of LLMs within each legal system before application.Here, we introduce KBL, a benchmark for assessing the Korean legal language understanding of LLMs, consisting of (1) 7 legal knowledge tasks (510 examples), (2) 4 legal reasoning tasks (288 examples), and (3) the Korean bar exam (4 domains, 53 tasks, 2,510 examples). First two datasets were developed in close collaboration with lawyers to evaluate LLMs in practical scenarios in a certified manner. Furthermore, considering legal practitioners’ frequent use of extensive legal documents for research, we assess LLMs in both a closed book setting, where they rely solely on internal knowledge, and a retrieval-augmented generation (RAG) setting, using a corpus of Korean statutes and precedents. The results indicate substantial room and opportunities for improvement.- Anthology ID:
- 2024.findings-emnlp.319
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5573–5595
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-emnlp.319/
- DOI:
- 10.18653/v1/2024.findings-emnlp.319
- Cite (ACL):
- Yeeun Kim, Youngrok Choi, Eunkyung Choi, JinHwan Choi, Hai Jin Park, and Wonseok Hwang. 2024. Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 5573–5595, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models (Kim et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-emnlp.319.pdf