mlsanityData to 14 Oct 2026

Evaluating Multimodal Generative AI with Korean Educational Standards

arXiv 2502.15422 ↗First posted 21 Feb 2025cs.CL cs.AI cs.CVcited by 2

Not on the map. Why?

Abstract (arXiv)

This paper presents the Korean National Educational Test Benchmark (KoNET), a new benchmark designed to evaluate Multimodal Generative AI Systems using Korean national educational tests. KoNET comprises four exams: the Korean Elementary General Educational Development Test (KoEGED), Middle (KoMGED), High (KoHGED), and College Scholastic Ability Test (KoCSAT). These exams are renowned for their rigorous standards and diverse questions, facilitating a comprehensive analysis of AI performance across different educational levels. By focusing on Korean, KoNET provides insights into model performance in less-explored languages. We assess a range of models - open-source, open-access, and closed APIs - by examining difficulties, subject diversity, and human error rates. The code and dataset builder will be made fully open-sourced at https://github.com/naver-ai/KoNET.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 18 of its 29 references that are in the map. First posted on arXiv 21 Feb 2025.

Builds on

18 of 29 references.

References · 18

  1. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  2. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  3. 2023Improved Baselines with Visual Instruction Tuningcited by 4,418
  4. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
  5. 2023MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIcited by 2,080
  6. 2022Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answeringcited by 1,919
  7. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,917
  8. 2023MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contextscited by 1,614
  9. 2024How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suitescited by 1,050
  10. 2016A Diagram Is Worth A Dozen Imagescited by 804
  11. 2024Yi: Open Foundation Models by 01.AIcited by 719
  12. 2024PaliGemma: A versatile 3B VLM for transfercited by 696
6 more
  1. 2024MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmarkcited by 512
  2. 2024CogVLM2: Visual Language Models for Image and Video Understandingcited by 217
  3. 2024KMMLU: Measuring Massive Multitask Language Understanding in Koreancited by 91
  4. 2024CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Masterycited by 30
  5. 2024Open Ko-LLM Leaderboard: Evaluating Large Language Models in Korean with Ko-H5 Benchmarkcited by 23
  6. 2024EXAONE 3.0 7.8B Instruction Tuned Language Modelcited by 21

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026HyperCLOVA X 8B Omnicited by 4
  2. 2026KMMMU: Evaluation of Massive Multi-discipline Multimodal Understanding in Korean Language and Contextcited by 0