@inproceedings{lee-etal-2026-redefining,
title = "Redefining Evaluation Standards: A Unified Framework for Evaluating the {K}orean Capabilities of Language Models",
author = "Lee, Hanwool and
Choi, Dasol and
Kim, Sooyong and
Jung, Ilgyun and
Baek, Sangwon and
Son, Guijin and
Hwang, Inseong and
Lee, Naeun and
Hong, Seunghyeok",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.345/",
doi = "10.63317/46t2asmno5ng",
pages = "4414--4422",
abstract = "Recent advancements in Korean large language models (LLMs) have driven numerous benchmarks and evaluation methods, yet inconsistent protocols cause up to 10 p.p performance gaps across institutions. Overcoming these reproducibility gaps does not mean enforcing a one-size-fits-all evaluation. Rather, effective benchmarking requires diverse experimental approaches and a framework robust enough to support them. To this end, we introduce HRET (Haerae Evaluation Toolkit), an open-source, registry-based framework that unifies Korean LLM assessment. HRET integrates major Korean benchmarks, multiple inference backends, and multi-method evaluation, with language consistency enforcement to ensure genuine Korean outputs. Its modular registry design also enables rapid incorporation of new datasets, methods, and backends, ensuring the toolkit adapts to evolving research needs. Beyond standard accuracy metrics, HRET incorporates Korean-focused output analyses-morphology-aware Type-Token Ratio (TTR) for evaluating lexical diversity and systematic keyword-omission detection for identifying missing concepts-to provide diagnostic insights into language-specific behaviors. These targeted analyses help researchers pinpoint morphological and semantic shortcomings in model outputs, guiding focused improvements in Korean LLM development."
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<abstract>Recent advancements in Korean large language models (LLMs) have driven numerous benchmarks and evaluation methods, yet inconsistent protocols cause up to 10 p.p performance gaps across institutions. Overcoming these reproducibility gaps does not mean enforcing a one-size-fits-all evaluation. Rather, effective benchmarking requires diverse experimental approaches and a framework robust enough to support them. To this end, we introduce HRET (Haerae Evaluation Toolkit), an open-source, registry-based framework that unifies Korean LLM assessment. HRET integrates major Korean benchmarks, multiple inference backends, and multi-method evaluation, with language consistency enforcement to ensure genuine Korean outputs. Its modular registry design also enables rapid incorporation of new datasets, methods, and backends, ensuring the toolkit adapts to evolving research needs. Beyond standard accuracy metrics, HRET incorporates Korean-focused output analyses-morphology-aware Type-Token Ratio (TTR) for evaluating lexical diversity and systematic keyword-omission detection for identifying missing concepts-to provide diagnostic insights into language-specific behaviors. These targeted analyses help researchers pinpoint morphological and semantic shortcomings in model outputs, guiding focused improvements in Korean LLM development.</abstract>
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%0 Conference Proceedings
%T Redefining Evaluation Standards: A Unified Framework for Evaluating the Korean Capabilities of Language Models
%A Lee, Hanwool
%A Choi, Dasol
%A Kim, Sooyong
%A Jung, Ilgyun
%A Baek, Sangwon
%A Son, Guijin
%A Hwang, Inseong
%A Lee, Naeun
%A Hong, Seunghyeok
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F lee-etal-2026-redefining
%X Recent advancements in Korean large language models (LLMs) have driven numerous benchmarks and evaluation methods, yet inconsistent protocols cause up to 10 p.p performance gaps across institutions. Overcoming these reproducibility gaps does not mean enforcing a one-size-fits-all evaluation. Rather, effective benchmarking requires diverse experimental approaches and a framework robust enough to support them. To this end, we introduce HRET (Haerae Evaluation Toolkit), an open-source, registry-based framework that unifies Korean LLM assessment. HRET integrates major Korean benchmarks, multiple inference backends, and multi-method evaluation, with language consistency enforcement to ensure genuine Korean outputs. Its modular registry design also enables rapid incorporation of new datasets, methods, and backends, ensuring the toolkit adapts to evolving research needs. Beyond standard accuracy metrics, HRET incorporates Korean-focused output analyses-morphology-aware Type-Token Ratio (TTR) for evaluating lexical diversity and systematic keyword-omission detection for identifying missing concepts-to provide diagnostic insights into language-specific behaviors. These targeted analyses help researchers pinpoint morphological and semantic shortcomings in model outputs, guiding focused improvements in Korean LLM development.
%R 10.63317/46t2asmno5ng
%U https://aclanthology.org/2026.lrec-1.345/
%U https://doi.org/10.63317/46t2asmno5ng
%P 4414-4422
Markdown (Informal)
[Redefining Evaluation Standards: A Unified Framework for Evaluating the Korean Capabilities of Language Models](https://aclanthology.org/2026.lrec-1.345/) (Lee et al., LREC 2026)
ACL
- Hanwool Lee, Dasol Choi, Sooyong Kim, Ilgyun Jung, Sangwon Baek, Guijin Son, Inseong Hwang, Naeun Lee, and Seunghyeok Hong. 2026. Redefining Evaluation Standards: A Unified Framework for Evaluating the Korean Capabilities of Language Models. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 4414–4422, Palma de Mallorca, Spain. ELRA Language Resource Association.