KFinEval-Pilot: A Comprehensive Benchmark Suite for Korean Financial Language Understanding

• URL: http://arxiv.org/abs/2504.13216v1
• Date: Thu, 17 Apr 2025 00:12:58 GMT
• Title: KFinEval-Pilot: A Comprehensive Benchmark Suite for Korean Financial Language Understanding
• Authors: Bokwang Hwang, Seonkyu Lim, Taewoong Kim, Yongjae Geun, Sunghyun Bang, Sohyun Park, Jihyun Park, Myeonggyu Lee, Jinwoo Lee, Yerin Kim, Jinsun Yoo, Jingyeong Hong, Jina Park, Yongchan Kim, Suhyun Kim, Younggyun Hahm, Yiseul Lee, Yejee Kang, Chanhyuk Yoon, Chansu Lee, Heeyewon Jeong, Jiyeon Lee, Seonhye Gu, Hyebin Kang, Yousang Cho, Hangyeol Yoo, KyungTae Lim,
• Abstract summary: KFinEval-Pilot is a benchmark suite specifically designed to evaluate large language models (LLMs) in the Korean financial domain.<n>It comprises over 1,000 curated questions across three critical areas: financial knowledge, legal reasoning, and financial toxicity.
• Score: 6.3604109210772934
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We introduce KFinEval-Pilot, a benchmark suite specifically designed to evaluate large language models (LLMs) in the Korean financial domain. Addressing the limitations of existing English-centric benchmarks, KFinEval-Pilot comprises over 1,000 curated questions across three critical areas: financial knowledge, legal reasoning, and financial toxicity. The benchmark is constructed through a semi-automated pipeline that combines GPT-4-generated prompts with expert validation to ensure domain relevance and factual accuracy. We evaluate a range of representative LLMs and observe notable performance differences across models, with trade-offs between task accuracy and output safety across different model families. These results highlight persistent challenges in applying LLMs to high-stakes financial applications, particularly in reasoning and safety. Grounded in real-world financial use cases and aligned with the Korean regulatory and linguistic context, KFinEval-Pilot serves as an early diagnostic tool for developing safer and more reliable financial AI systems.

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