Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
Summary
This research paper proposes two methods for improving dense document embeddings, which are crucial for neural retrieval. The first method introduces a contextual training procedure that explicitly incorporates neighboring documents into the contrastive learning process. This approach aims to create embeddings that can distinguish between documents even in challenging contexts. The second method introduces a contextual architecture that embeds information about neighboring documents into the encoded representation. The paper demonstrates that both methods achieve better performance than standard biencoders, especially in out-of-domain settings. Through experimentation and analysis, the authors confirm that their proposed methods significantly improve text embedding performance across various retrieval tasks.
原文链接：arxiv.org
