HRET: A Self-Evolving LLM Evaluation Toolkit for Korean

• URL: http://arxiv.org/abs/2503.22968v2
• Date: Tue, 01 Apr 2025 12:37:16 GMT
• Title: HRET: A Self-Evolving LLM Evaluation Toolkit for Korean
• Authors: Hanwool Lee, Soo Yong Kim, Dasol Choi, SangWon Baek, Seunghyeok Hong, Ilgyun Jeong, Inseon Hwang, Naeun Lee, Guijin Son,
• Abstract summary: We introduce HRET Haerae Evaluation Toolkit, an open-source, self-evolving evaluation framework tailored specifically for Korean LLMs.<n>HRET unifies diverse evaluation methods, including logit-based scoring, exact-match, language-inconsistency penalization, and LLM-as-a-Judge assessments.<n>With automated pipelines for continuous evolution, HRET provides a robust foundation for reproducible, fair, and transparent Korean NLP research.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recent advancements in Korean large language models (LLMs) have spurred numerous benchmarks and evaluation methodologies, yet the lack of a standardized evaluation framework has led to inconsistent results and limited comparability. To address this, we introduce HRET Haerae Evaluation Toolkit, an open-source, self-evolving evaluation framework tailored specifically for Korean LLMs. HRET unifies diverse evaluation methods, including logit-based scoring, exact-match, language-inconsistency penalization, and LLM-as-a-Judge assessments. Its modular, registry-based architecture integrates major benchmarks (HAE-RAE Bench, KMMLU, KUDGE, HRM8K) and multiple inference backends (vLLM, HuggingFace, OpenAI-compatible endpoints). With automated pipelines for continuous evolution, HRET provides a robust foundation for reproducible, fair, and transparent Korean NLP research.

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