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

• URL: http://arxiv.org/abs/2503.22968v4
• Date: Tue, 08 Jul 2025 13:13:04 GMT
• Title: Redefining Evaluation Standards: A Unified Framework for Evaluating the Korean Capabilities of Language Models
• Authors: Hanwool Lee, Dasol Choi, Sooyong Kim, Ilgyun Jung, Sangwon Baek, Guijin Son, Inseon Hwang, Naeun Lee, Seunghyeok Hong,
• Abstract summary: We introduce HRET (Haerae Evaluation Toolkit), an open-source, registry-based framework that unifies Korean assessment.<n>HRET integrates major Korean benchmarks, multiple inference backends, and multi-method evaluation.<n>Its modular registry design also enables rapid incorporation of new datasets, methods, and backends.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• CogBench: A Large Language Model Benchmark for Multilingual Speech-Based Cognitive Impairment Assessment [13.74065648648307]
  We propose CogBench, the first benchmark designed to evaluate the cross-lingual and cross-site generalizability of large language models for speech-based cognitive impairment assessment.<n>Our results show that conventional deep learning models degrade substantially when transferred across domains.<n>Our findings offer a critical step toward building clinically useful and linguistically robust speech-based cognitive assessment tools.
  arXiv  Detail & Related papers  (2025-08-05T12:06:16Z)
• OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics [101.78963920333342]
  We introduce OpenUnlearning, a standardized framework for benchmarking large language models (LLMs) unlearning methods and metrics.<n>OpenUnlearning integrates 9 unlearning algorithms and 16 diverse evaluations across 3 leading benchmarks.<n>We also benchmark diverse unlearning methods and provide a comparative analysis against an extensive evaluation suite.
  arXiv  Detail & Related papers  (2025-06-14T20:16:37Z)
• Foundations and Evaluations in NLP [1.0619039878979954]
  This memoir explores two fundamental aspects of Natural Language Processing (NLP): the creation of linguistic resources and the evaluation of NLP system performance.<n>My work has focused on developing a morpheme-based annotation scheme for the Korean language that captures linguistic properties from morphology to semantics.<n>I have proposed a novel evaluation framework, the jp-algorithm, which introduces an alignment-based method to address challenges in preprocessing tasks.
  arXiv  Detail & Related papers  (2025-04-02T04:14:03Z)
• Beyond the Singular: The Essential Role of Multiple Generations in Effective Benchmark Evaluation and Analysis [10.133537818749291]
  Large language models (LLMs) have demonstrated significant utilities in real-world applications.<n> Benchmark evaluations are crucial for assessing the capabilities of LLMs.
  arXiv  Detail & Related papers  (2025-02-13T03:43:33Z)
• StructTest: Benchmarking LLMs' Reasoning through Compositional Structured Outputs [78.84060166851805]
  StructTest is a novel benchmark that evaluates large language models (LLMs) on their ability to follow compositional instructions and generate structured outputs.<n> Assessments are conducted deterministically using a rule-based evaluator, which can be easily extended to new tasks and datasets.<n>We demonstrate that StructTest remains challenging even for top-performing models like Deepseek-V3/R1 and GPT-4o.
  arXiv  Detail & Related papers  (2024-12-23T22:08:40Z)
• The Vulnerability of Language Model Benchmarks: Do They Accurately Reflect True LLM Performance? [1.3810901729134184]
  Large Language Models (LLMs) excel at standardized tests while failing to demonstrate genuine language understanding and adaptability.<n>Our systematic analysis of NLP evaluation frameworks reveals pervasive vulnerabilities across the evaluation spectrum.<n>We lay the groundwork for new evaluation methods that resist manipulation, minimize data contamination, and assess domain-specific tasks.
  arXiv  Detail & Related papers  (2024-12-02T20:49:21Z)
• A Preliminary Study of Multilingual Code Language Models for Code Generation Task Using Translated Benchmarks [0.0]
  We evaluate the performance of Poly-Coder, a pioneering open-source, multilingual CLM built for code generation. Our results suggest that the outcomes observed in these translated benchmarks align well with evaluation metrics used during the training phase. These initial insights highlight the need for more comprehensive empirical studies.
  arXiv  Detail & Related papers  (2024-11-23T06:40:47Z)
• MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models [71.36392373876505]
  We introduce MMIE, a large-scale benchmark for evaluating interleaved multimodal comprehension and generation in Large Vision-Language Models (LVLMs) MMIE comprises 20K meticulously curated multimodal queries, spanning 3 categories, 12 fields, and 102 subfields, including mathematics, coding, physics, literature, health, and arts. It supports both interleaved inputs and outputs, offering a mix of multiple-choice and open-ended question formats to evaluate diverse competencies.
  arXiv  Detail & Related papers  (2024-10-14T04:15:00Z)
• Pretraining Data Detection for Large Language Models: A Divergence-based Calibration Method [108.56493934296687]
  We introduce a divergence-based calibration method, inspired by the divergence-from-randomness concept, to calibrate token probabilities for pretraining data detection.<n>We have developed a Chinese-language benchmark, PatentMIA, to assess the performance of detection approaches for LLMs on Chinese text.
  arXiv  Detail & Related papers  (2024-09-23T07:55:35Z)
• Benchmarks as Microscopes: A Call for Model Metrology [76.64402390208576]
  Modern language models (LMs) pose a new challenge in capability assessment. To be confident in our metrics, we need a new discipline of model metrology.
  arXiv  Detail & Related papers  (2024-07-22T17:52:12Z)
• FineSurE: Fine-grained Summarization Evaluation using LLMs [22.62504593575933]
  FineSurE is a fine-grained evaluator specifically tailored for the summarization task using large language models (LLMs) It also employs completeness and conciseness criteria, in addition to faithfulness, enabling multi-dimensional assessment.
  arXiv  Detail & Related papers  (2024-07-01T02:20:28Z)
• DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation [75.81096662788254]
  Large Language Models (LLMs) are scalable and economical evaluators. The question of how reliable these evaluators are has emerged as a crucial research question. We propose Decompose and Aggregate, which breaks down the evaluation process into different stages based on pedagogical practices.
  arXiv  Detail & Related papers  (2024-05-24T08:12:30Z)
• Evaluating Generative Language Models in Information Extraction as Subjective Question Correction [49.729908337372436]
  We propose a new evaluation method, SQC-Score. Inspired by the principles in subjective question correction, we propose a new evaluation method, SQC-Score. Results on three information extraction tasks show that SQC-Score is more preferred by human annotators than the baseline metrics.
  arXiv  Detail & Related papers  (2024-04-04T15:36:53Z)
• MERA: A Comprehensive LLM Evaluation in Russian [43.02318109348788]
  We introduce an open Multimodal Evaluation of Russian-language Architectures (MERA) benchmark for evaluating foundation models. The benchmark encompasses 21 evaluation tasks for generative models in 11 skill domains. We propose an evaluation methodology, an open-source code base for the MERA assessment, and a leaderboard with a submission system.
  arXiv  Detail & Related papers  (2024-01-09T12:55:21Z)
• DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models [76.79929883963275]
  DIALIGHT is a toolkit for developing and evaluating multilingual Task-Oriented Dialogue (ToD) systems. It features a secure, user-friendly web interface for fine-grained human evaluation at both local utterance level and global dialogue level. Our evaluations reveal that while PLM fine-tuning leads to higher accuracy and coherence, LLM-based systems excel in producing diverse and likeable responses.
  arXiv  Detail & Related papers  (2024-01-04T11:27:48Z)
• Self-Evaluation Improves Selective Generation in Large Language Models [54.003992911447696]
  We reformulate open-ended generation tasks into token-level prediction tasks. We instruct an LLM to self-evaluate its answers. We benchmark a range of scoring methods based on self-evaluation.
  arXiv  Detail & Related papers  (2023-12-14T19:09:22Z)
• AlignBench: Benchmarking Chinese Alignment of Large Language Models [99.24597941555277]
  We introduce AlignBench, a comprehensive benchmark for evaluating Chinese Large Language Models' alignment. We design a human-in-the-loop data curation pipeline, containing eight main categories, 683 real-scenario rooted queries and corresponding human verified references. For automatic evaluation, our benchmark employs a rule-calibrated multi-dimensional LLM-as-Judgecitezheng2023judging approach with Chain-of-Thought to generate explanations and final ratings.
  arXiv  Detail & Related papers  (2023-11-30T17:41:30Z)
• Learning Evaluation Models from Large Language Models for Sequence Generation [61.8421748792555]
  We propose a three-stage evaluation model training method that utilizes large language models to generate labeled data for model-based metric development.<n> Experimental results on the SummEval benchmark demonstrate that CSEM can effectively train an evaluation model without human-labeled data.
  arXiv  Detail & Related papers  (2023-08-08T16:41:16Z)
• Generating Benchmarks for Factuality Evaluation of Language Models [61.69950787311278]
  We propose FACTOR: Factual Assessment via Corpus TransfORmation, a scalable approach for evaluating LM factuality. FACTOR automatically transforms a factual corpus of interest into a benchmark evaluating an LM's propensity to generate true facts from the corpus vs. similar but incorrect statements. We show that: (i) our benchmark scores increase with model size and improve when the LM is augmented with retrieval; (ii) benchmark score and perplexity do not always agree on model ranking; (iii) when perplexity and benchmark score disagree, the latter better reflects factuality in open-ended generation.
  arXiv  Detail & Related papers  (2023-07-13T17:14:38Z)
• KOBEST: Korean Balanced Evaluation of Significant Tasks [3.664687661363732]
  A well-formulated benchmark plays a critical role in spurring advancements in the natural language processing (NLP) field. We propose a new benchmark named Korean balanced evaluation of significant tasks (KoBEST), which consists of five Korean-language downstream tasks.
  arXiv  Detail & Related papers  (2022-04-09T20:13:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.