Throughout schooling, students are tested on reading comprehension and logical reasoning. Students have developed various strategies for completing such exams, some of which are generally thought to outperform others. One such strategy involves emphasizing relative accuracy over absolute accuracy and can theoretically produce the correct answer without full knowledge of the information required to solve the question. This paper examines the effectiveness of applying such a strategy to train transfer learning models to solve reading comprehension and logical reasoning questions. The models were evaluated on the ReClor dataset, a challenging reading comprehension and logical reasoning benchmark. While previous studies targeted logical reasoning skills, we focus on a general training method and model architecture. We propose the polytuplet loss function, an extension of the triplet loss function, to ensure prioritization of learning the relative correctness of answer choices over learning the true accuracy of each choice. Our results indicate that models employing polytuplet loss outperform existing baseline models. Although polytuplet loss is a promising alternative to other contrastive loss functions, further research is required to quantify the benefits it may present.

翻译：在求学过程中，学生需通过考试检验阅读理解与逻辑推理能力。学生发展出多种应试策略，其中部分策略普遍被认为优于其他策略。一种典型策略强调相对准确性优于绝对准确性，理论上可在不完全掌握解题所需信息的情况下得出正确答案。本文旨在探究将该策略应用于训练迁移学习模型以解决阅读理解与逻辑推理问题的有效性。模型在具有挑战性的阅读理解与逻辑推理基准数据集ReClor上进行了评估。现有研究主要聚焦于逻辑推理技能，而本文则关注通用训练方法与模型架构。我们提出多组损失函数（polytuplet loss function），该函数作为三元组损失函数的扩展，旨在优先学习答案选项的相对正确性而非每个选项的真实准确性。实验结果表明，采用多组损失函数的模型性能优于现有基线模型。尽管多组损失函数可作为对比损失函数的有效替代方案，但量化其潜在优势仍需进一步研究。