exaone
Datasets
All datasets matching “exaone”KMMLU-Pro
KMMLU-Pro
📄 Paper | 🖥️ Code
We introduce KMMLU-PRO, a challenging new benchmark comprising 2,822 problems from the official exams for Korean National Professional Licensure (KNPL), representing highly specialized professions in Korea. To bridge the gap between LLM performance and real-world applicability, our evaluation closely mirrors the official certification criteria including reporting the number of professional licenses each LLM could pass according to these standards. All… See the full description on the dataset page: https://huggingface.co/datasets/LGAI-EXAONE/KMMLU-Pro.FinTexTSKMMLU-Redux
KMMLU-Redux
📄 Paper
We introduce KMMLU-Redux, a reconstructed version of the existing KMMLU, comprising 2,587 problems from Korean National Technical Qualification (KNTQ) exams. We identified several critical issues in the KMMLU, including leaked answers, lack of clarity, ill-posed questions, notation errors, and contamination risks. To address these problems, we conducted rigorous manual examination and decontaminated the dataset from the pre-training corpus to prevent potential… See the full description on the dataset page: https://huggingface.co/datasets/LGAI-EXAONE/KMMLU-Redux.MANTA-1M
Abstract
We introduce MANTA, an automated pipeline that generates high-quality large-scale instruction fine-tuning datasets from massive web corpora while preserving their diversity and scalability. By extracting structured syllabi from web documents and leveraging high-performance LLMs, our approach enables highly effective query-response generation with minimal human intervention. Extensive experiments on 8B-scale LLMs demonstrate that fine-tuning on the MANTA-1M dataset… See the full description on the dataset page: https://huggingface.co/datasets/LGAI-EXAONE/MANTA-1M.KoMT-Bench
KoMT-Bench
Introduction
We present KoMT-Bench, a benchmark designed to evaluate the capability of language models in following instructions in Korean.
KoMT-Bench is an in-house dataset created by translating MT-Bench [1] dataset into Korean and modifying some questions to reflect the characteristics and cultural nuances of the Korean language.
After the initial translation and modification, we requested expert linguists to conduct a thorough review of our benchmark… See the full description on the dataset page: https://huggingface.co/datasets/LGAI-EXAONE/KoMT-Bench.Ko-LongRAG
Abstract
The rapid advancement of large language models (LLMs) significantly enhances long-context Retrieval-Augmented Generation (RAG), yet existing benchmarks focus primarily on English. This leaves low-resource languages without comprehensive evaluation frameworks, limiting their progress in retrieval-based tasks. To bridge this gap, we introduce Ko-LongRAG, the first Korean long-context RAG benchmark. Unlike conventional benchmarks that depend on external retrievers… See the full description on the dataset page: https://huggingface.co/datasets/LGAI-EXAONE/Ko-LongRAG.