Summary of Developing a Pragmatic Benchmark For Assessing Korean Legal Language Understanding in Large Language Models, by Yeeun Kim et al.
Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models
by Yeeun Kim, Young Rok Choi, Eunkyung Choi, Jinhwan Choi, Hai Jin Park, Wonseok Hwang
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel benchmark, KBL, is introduced to evaluate the ability of large language models (LLMs) to understand Korean legal language, comprising three tasks: 7 legal knowledge tasks, 4 legal reasoning tasks, and the Korean bar exam. The datasets were developed in collaboration with lawyers to assess LLMs in practical scenarios. The performance of LLMs is evaluated in both a closed-book setting and a retrieval-augmented generation (RAG) setting, using a corpus of Korean statutes and precedents. The results indicate opportunities for improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have been tested on legal tasks, but their performance varies greatly depending on the task and language used. To help fix this problem, researchers created a new test called KBL to see how well LLMs can understand Korean law. This test includes several types of questions that require LLMs to use legal knowledge in practical ways. The results show that there is still room for improvement. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
