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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.
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.
Explainable Supporting Facts Generation via Query-Focused Multi-Document Summarization for Open Domain Question Answering Model
http://doi.org/10.5626/JOK.2024.51.11.1020
"Open domain question answering system requires external knowledge not satisfied by knowledge inherent in the language model to answer a given query. It is a technology that is being studied importantly for solving the hallucination problem that occurs in recent large language models. In this paper, we propose a model that utilizes structural information of Query-attentive Semantic Graph (QSG) to summarize information between distant documents based on a query and utilize it as supporting factors for a multi-document-based question answering system. Query-based supporting factors generated by summarizing can improve answer generation performance and show better explainability than extracted supporting factors."
Performance Improvement of a Korean Open Domain Q&A System by Applying the Trainable Re-ranking and Response Filtering Model
Hyeonho Shin, Myunghoon Lee, Hong-Woo Chun, Jae-Min Lee, Sung-Pil Choi
http://doi.org/10.5626/JOK.2023.50.3.273
Research on Open Domain Q&A, which can identify answers to user inquiries without preparing the target paragraph in advance, is currently being undertaken as deep learning technology is used for natural language processing. However, existing studies have limitations in semantic matching using keyword-based information retrieval. To supplement this, deep learning-based information retrieval research is in progress. But there are not many domestic studies that have been empirically applied to real systems. In this paper, a two-step performance enhancement method was proposed to improve the performance of the Korean open domain Q&A system. The proposed method is a method of sequentially applying a machine learning-based re-ranking model and a response filtering model to a baseline system in which a search engine and an MRC model was combined. In the case of the baseline system, the initial performance was an F1 score of 74.43 and an EM score of 60.79, and it was confirmed that the performance improved to an F1 score of 82.5 and an EM score of 68.82 when the proposed method was used.
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