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KLUE (Korean Language Understanding Evaluation)

Introduced by Sungjoon Park et al. in KLUE: Korean Language Understanding Evaluation20 May 2021 archive 2025-07-28

Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language understanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible to anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain unambiguous annotations for all datasets. Furthermore, we build an evaluation system and carefully choose evaluations metrics for every task, thus establishing fair comparison across Korean language models.

KLUE benchmark is composed of 8 tasks:

  • Topic Classification (TC)
  • Sentence Textual Similarity (STS)
  • Natural Language Inference (NLI)
  • Named Entity Recognition (NER)
  • Relation Extraction (RE)
  • (Part-Of-Speech) + Dependency Parsing (DP)
  • Machine Reading Comprehension (MRC)
  • Dialogue State Tracking (DST)

Benchmarks archive 2025-07-28

All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Text Classification KLUE no rows — — 0 Compare

Papers archive 2025-07-28

No paper in the archive has a leaderboard row on this dataset; the archive counts 21 papers for it but never published that list.

Dataset loaders archive 2025-07-28

3 loaders as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

No licence recorded in the archive. Absence here is not a statement about the dataset's terms.

Modalities archive 2025-07-28

Languages archive 2025-07-28

Variants archive 2025-07-28

  • KLUE

1 variant name, as the archive lists them.

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