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Found 7 papers, 0 papers with code
A Study on How Attention Scores in the BERT Model are Aware of Lexical Categories in Syntactic and Semantic Tasks on the GLUE Benchmark
This study examines whether the attention scores between tokens in the BERT model significantly vary based on lexical categories during the fine-tuning process for downstream tasks.
KIT-19: A Comprehensive Korean Instruction Toolkit on 19 Tasks for Fine-Tuning Korean Large Language Models
Based on the its quality and empirical results, this paper proposes that \textit{KIT-19} has the potential to make a substantial contribution to the future improvement of Korean LLMs' performance.
Korean Bio-Medical Corpus (KBMC) for Medical Named Entity Recognition
Named Entity Recognition (NER) plays a pivotal role in medical Natural Language Processing (NLP).
Medical Named Entity Recognition
named-entity-recognition
+2
CARBD-Ko: A Contextually Annotated Review Benchmark Dataset for Aspect-Level Sentiment Classification in Korean
In order to tackle these challenges, we introduce CARBD-Ko (a Contextually Annotated Review Benchmark Dataset for Aspect-Based Sentiment Classification in Korean), a benchmark dataset that incorporates aspects and dual-tagged polarities to distinguish between aspect-specific and aspect-agnostic sentiment classification.
Automatic Construction of a Korean Toxic Instruction Dataset for Ethical Tuning of Large Language Models
Caution: this paper may include material that could be offensive or distressing.
Mergen: The First Manchu-Korean Machine Translation Model Trained on Augmented Data
The Manchu language, with its roots in the historical Manchurian region of Northeast China, is now facing a critical threat of extinction, as there are very few speakers left.
DaG LLM ver 1.0: Pioneering Instruction-Tuned Language Modeling for Korean NLP
This paper presents the DaG LLM (David and Goliath Large Language Model), a language model specialized for Korean and fine-tuned through Instruction Tuning across 41 tasks within 13 distinct categories.
