“Multi-Hop Question Answering (MHQA) is a significant area in question answering, requiringmultiple reasoning components, including document retrieval, supporting sentence prediction,and answer span extraction. In this work, we present the first application of label smoothing tothe MHQA task, aiming to enhance generalization capabilities in MHQA systems while miti-gating overfitting of answer spans and reasoning paths in the training set. We introduce a novellabel smoothing technique, F1 Smoothing, which incorporates uncertainty into the learning pro-cess and is specifically tailored for Machine Reading Comprehension (MRC) tasks. Moreover,we employ a Linear Decay Label Smoothing Algorithm (LDLA) in conjunction with curricu-lum learning to progressively reduce uncertainty throughout the training process. Experimenton the HotpotQA dataset confirms the effectiveness of our approach in improving generaliza-tion and achieving significant improvements, leading to new state-of-the-art performance on theHotpotQA leaderboard.”

“本文介绍了参赛系统在第三届中文空间语义理解评测(SpaCE2023)采用的技术路线:面向空间语义异常识别任务提出了抽取方法,并结合生成器进一步完成了空间语义角色标注任务,空间场景异同判断任务则使用了大语言模型生成。本文进一步探索了大语言模型在评测数据集上的应用,发现指令设计是未来工作的重点和难点。参赛系统的代码和模型见https://github.com/ShacklesLay/Space2023。”