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A Retrieval Augmented Generation(RAG) System Using Query Rewritting Based on Large Langauge Model(LLM)
Minsu Han, Seokyoung Hong, Myoung-Wan Koo
http://doi.org/10.5626/JOK.2025.52.6.474
This paper proposes a retrieval pipeline that can be effectively utilized in fields requiring expert knowledge without requiring fine-tuning. To achieve high accuracy, we introduce a query rewriting retrieval method that leverages large language models to generate examples similar to the given question, achieving higher similarity than existing retrieval models. The proposed method demonstrates excellent performance in both automated evaluations and expert qualitative assessments, while also providing explainability in retrieval results through generated examples. Additionally, we suggest prompts that can be utilized in various domains requiring specialized knowledge during the application of this method. Furthermore, we propose a pipeline method that incorporates a Top-1 retrieval model, which chooses the most relevant document from the three returned by the query rewriting retrieval model. This aims to prevent the hallucination issue caused by the input of unnecessary documents into the large language model.
Enhancing Passage Selection and Answer Generation in FiD Systems Using Relevance Gating
Seung-ho Choi, Shihyun Park, Minsang Kim, Chansol Park, Junho Wang, Ji-Yoon Kim, Bong-Su Kim
http://doi.org/10.5626/JOK.2025.52.5.385
In this paper, we proposed a novel approach to enhance the performance of the Fusion-in-Decoder (FiD) model in open-domain question answering systems. The FiD model operates by independently encoding multiple passages and then combining them during the decoding stage to generate answers. However, this method has the drawback of not filtering out passages containing unnecessary information, thereby placing an excessive burden on the decoder. To address this issue, we introduced a Relevance Gate inspired by the forget gate of Long Short-Term Memory (LSTM). This gate can evaluate the relevance of each passage in parallel, selectively transmitting information to the decoder, thereby significantly improving the accuracy and efficiency of answer generation. Additionally, we applied a new activation function suitable for open-domain question answering systems instead of the sigmoid function to ensure the model's stability.
Study on the Evaluation of Embedding Models in the Natural Language Processing
http://doi.org/10.5626/JOK.2025.52.2.141
This paper applies embedding techniques to key tasks in the field of Natural Language Processing (NLP), including semantic textual search, text classification, question answering, and clustering, and evaluates their performance. Recently, with the advancement of large-scale language models, embedding technologies have played a crucial role in various NLP applications. Several types of embedding models have been publicly released, and this paper assesses the performance of these models. For this evaluation, vector representations generated by embedding models were used as an intermediate step for each selected task. The experiments utilized publicly available Korean and English datasets, and five NLP tasks were defined. Notably, the BGE-M3 model, which demonstrated exceptional performance in multilingual, cross-lingual, and long-document retrieval tasks, was a key focus of this study. The experimental results show that the BGE-M3 model outperforms other models in three of the evaluated NLP tasks. The findings of this research are expected to provide guidance in selecting embedding models for identifying similar sentences or documents in recent Retrieval-Augmented Generation (RAG) applications.
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