Seung-ho Choi  Shihyun Park  Minsang Kim  Chansol Park  Junho Wang  Ji-Yoon Kim  Bong-Su Kim

10.5626/JOK.2025.52.5.385

ODQA RAG encoder-decoder language model question-answering model

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.