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.