mlsanityData to 7 Oct 2026

GLAN-QnA-KR: A Seedless Taxonomy-Driven Korean Instruction Corpus

arXiv 2607.20443 ↗First posted 12 May 2026cs.CL cs.LGcited by 0

On the map: Mixture-of-experts → MoE large language models

Abstract (arXiv)

We release GLAN-QnA-KR, a 303,581-row openly redistributable Korean instruction-QA corpus produced via the seedless taxonomy-driven GLAN synthesis pipeline with Microsoft's Phi-3.5-MoE-instruct as the producer model (generation: 2024-12; release: 2024-12; licence: OpenRAIL). The corpus spans a flat taxonomy of 1,084 English-labelled disciplines paired with Korean question/answer text, a 100-900 difficulty scale, and a median of 313 question characters and 1,098 answer characters per record. Two properties are atypical for synthetic instruction data at this scale: (i) exact duplicate questions number only 1 in 303,581 rows and character-trigram near-duplicate clusters at Jaccard >= 0.9 number zero in a 5,000-sample probe, and (ii) a two-layer contamination audit against KMMLU, KoBEST (five sub-tasks), and HAE-RAE-Bench shows a maximum test-vs-corpus question-level character-trigram Jaccard of 0.163 with zero test items at Jaccard >= 0.7, and a maximum multilingual-E5 cosine of 0.901 with a single test item at cosine >= 0.90 and zero at >= 0.95, across 20,000 sampled GLAN questions and seven evaluation sets. At the time of release, this is, to our knowledge, the largest single-pipeline synthetic Korean instruction corpus verifiable on the Hugging Face Hub and the only Korean >=100k-row corpus built under a seedless taxonomy-driven protocol. This note documents the generation protocol, corpus statistics, the contamination audit, and the licensing boundary in a form suitable for downstream citation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in MoE large language models, one of 12 directions in Mixture-of-experts. Builds on 11 of its 18 references that are in the map. First posted on arXiv 12 May 2026.

Builds on

11 of 18 references.

References · 11

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  3. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,883
  4. 2023Textbooks Are All You Needcited by 561
  5. 2024Multilingual E5 Text Embeddings: A Technical Reportcited by 357
  6. 2023Orca 2: Teaching Small Language Models How to Reasoncited by 157
  7. 2023What's In My Big Data?cited by 129
  8. 2024Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Modelscited by 99
  9. 2024KMMLU: Measuring Massive Multitask Language Understanding in Koreancited by 91
  10. 2023HAE-RAE Bench: Evaluation of Korean Knowledge in Language Modelscited by 47
  11. 2022KOBEST: Korean Balanced Evaluation of Significant Taskscited by 37

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  1. 2024Rapidly Developing High-quality Instruction Data and Evaluation Benchmark for Large Language Models with Minimal Human Effort: A Case Study on Japanese
  2. 2025KORMo: Korean Open Reasoning Model for Everyone
  3. 2023Self-QA: Unsupervised Knowledge Guided Language Model Alignment
  4. 2024Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai
  5. 2025Ko-PIQA: A Korean Physical Commonsense Reasoning Dataset with Cultural Context
  6. 2025CYCLE-INSTRUCT: Fully Seed-Free Instruction Tuning via Dual Self-Training and Cycle Consistency
  7. 2024Rethinking Data Synthesis: A Teacher Model Training Recipe with Interpretation
  8. 2025Ko-MuSR: A Multistep Soft Reasoning Benchmark for LLMs Capable of Understanding Korean