We present LinkNav, an enhanced experience for reading academic papers which makes explicit connections between related but non-adjacent passages. To create the experience, we instruct a language model to generate questions that may arise while reading a passage and then search for answer passages elsewhere in the document, forming intra-document connections when answers are found. We confirm that these building blocks work well to power the experience, with an answer detection pipeline that works with high precision, resulting in a reasonable number of connections being made for a document. On a dataset of academic papers, we find that connected passages are on average ten segments away from each other, making explicit connections that a reader may have otherwise missed.

翻译：我们提出LinkNav，一种增强的学术论文阅读体验，能够明确呈现相关但不相邻段落之间的关联。为实现该体验，我们引导语言模型生成阅读某段落时可能提出的问题，并在文档其他位置搜索答案段落，当找到答案时建立文档内部的关联。我们证实这些基础组件能有效支撑该体验：答案检测流程在保持高精度的同时，可为文档建立合理数量的关联。在学术论文数据集上的实验表明，被关联的段落平均相隔十个段落间隔，这种显式连接能帮助读者捕捉到可能被忽略的潜在关联。