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Enhancing Retrieval-Augmented Generation Through Zero-Shot Sentence-Level Passage Refinement with LLMs
Taeho Hwang, Soyeong Jeong, Sukmin Cho, Jong C. Park
http://doi.org/10.5626/JOK.2025.52.4.304
This study presents a novel methodology designed to enhance the performance and effectiveness of Retrieval-Augmented Generation (RAG) by utilizing Large Language Models (LLMs) to eliminate irrelevant content at the sentence level from retrieved documents. This approach refines the content of passages exclusively through LLMs, avoiding the need for additional training or data, with the goal of improving the performance in knowledge-intensive tasks. The proposed method was tested in an open-domain question answering (QA) environment, where it demonstrated its ability to effectively remove unnecessary content and outperform over traditional RAG methods. Overall, our approach has proven effective in enhancing performance compared to conventional RAG techniques and has shown the capability to improve RAG's accuracy in a zero-shot setting without requiring additional training data.
Calibration of Pre-trained Language Model for the Korean Language
Soyeong Jeong, Wonsuk Yang, ChaeHun Park, Jong C. Park
http://doi.org/10.5626/JOK.2021.48.4.434
The development of deep learning models has continuously demonstrated performance beyond humans reach in various tasks such as computer vision and natural language understanding tasks. In particular, pre-trained Transformer models have recently shown remarkable performance in natural language understanding problems such as question answering (QA) tasks and dialogue tasks. However, despite the rapid development of deep learning models such as Transformer-based models, the underlying mechanisms of action remain relatively unknown. As a method of analyzing deep learning models, calibration of models measures the extent of matching of the predicted value of the model (confidence) with the actual value (accuracy). Our study aims at interpreting pre-trained Korean language models based on calibration. In particular, we have analyzed whether pre-trained Korean language models can capture ambiguities in sentences and applied the smoothing methods to quantitatively measure such ambiguities with confidence. In addition, in terms of calibration, we have evaluated the capability of pre-trained Korean language models in identifying grammatical characteristics in the Korean language, which affect semantic changes in the Korean sentences.
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