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KULLM: Learning to Construct Korean Instruction-Following Large Language Models
Seungjun Lee, Yoonna Jang, Jeongwook Kim, Taemin Lee, Heuiseok Lim
http://doi.org/10.5626/JOK.2024.51.9.817
The emergence of Large Language Models (LLMs) has revolutionized the research paradigm in natural language processing. While instruction-tuning techniques have been pivotal in enhancing LLM performance, the majority of current research has focused predominantly on English. This study addresses the need for multilingual approaches by presenting a method for developing and evaluating Korean instruction-following models. We fine-tuned LLM models using Korean instruction datasets and conducted a comprehensive performance analysis using various dataset combinations. The resulting Korean instruction-following model is made available as an open-source resource, contributing to the advancement of Korean LLM research. Our work aims to bridge the language gap in LLM development and promote more inclusive AI technologies.
Cross-Validated Ensemble Methods in Natural Language Inference
Kisu Yang, Taesun Whang, Dongsuk Oh, Chanjun Park, Heuiseok Lim
http://doi.org/10.5626/JOK.2021.48.2.154
An ensemble method is a machine learning technique that combines several models to make the final prediction, which guarantees improved performance for deep learning models. However, most techniques require additional models or operations only for an ensemble. To address this problem, we propose a cross-validated ensemble method for reducing the costs of ensemble operations with cross-validation and for improving the generalization effects with the ensemble. To demonstrate the effectiveness of the proposed method, we show the improved performances of the proposed ensemble over the previous ensemble methods using the BiLSTM, CNN, ELMo and BERT models on the MRPC and RTE datasets. We also discuss the generalization mechanism involved in cross-validation along with the performance changes caused by the hyper-parameter of cross-validation.
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