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Redefining Evaluation Standards: A Unified Framework for Evaluating the Korean Capabilities of Language Models

Created by
  • Haebom

Author

Hanwool Lee, Dasol Choi, Sooyong Kim, Ilgyun Jung, Sangwon Baek, Guijin Son, Inseon Hwang, Naeun Lee, Seunghyeok Hong

Outline

In this paper, we present an open-source evaluation framework, the Haerae Evaluation Toolkit (HRET), to address the reproducibility issue in the performance evaluation of Korean large-scale language models (LLMs). HRET integrates major Korean benchmarks, various inference backends, and multiple evaluation methods, and adopts a modular registry design that maintains the consistency of Korean output and allows rapid integration of new datasets, methods, and backends. In addition to standard accuracy metrics, it diagnoses morphological and semantic defects in the model output and suggests ways to improve them through Korean-specific analyses such as morphological recognition type-to-token ratio (TTR) and keyword omission detection.

Takeaways, Limitations

Takeaways:
Contributes to solving the problem of reproducibility of Korean LLM assessments.
Integrates various evaluation methods and benchmarks to enable comprehensive evaluation.
Modular design enables rapid integration of new datasets, methods, and backends.
Through Korean-specific analysis, we diagnose the model's linguistic defects and suggest ways to improve them.
It is provided as open source, increasing accessibility for researchers.
Limitations:
Additional experiments and validation of the performance and efficiency of HRET are needed.
The scope of current integrated benchmarking and evaluation methodologies may be limited.
New datasets and methods must be continuously added and maintained.
Further research is needed to determine the generalizability of Korean-specific analyses.
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