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Found 4 papers, 3 papers with code
KMMLU: Measuring Massive Multitask Language Understanding in Korean
We propose KMMLU, a new Korean benchmark with 35, 030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM.
HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models
Large language models (LLMs) trained on massive corpora demonstrate impressive capabilities in a wide range of tasks.
EaSyGuide : ESG Issue Identification Framework leveraging Abilities of Generative Large Language Models
These outcomes underscore the effectiveness of our methodology in identifying ESG issues in news articles across different languages.
Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance
Extraction of sentiment signals from news text, stock message boards, and business reports, for stock movement prediction, has been a rising field of interest in finance.
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