让可微分检索索引同时兼顾相关性与多样性，无需后处理。

Differentiable Search Indexing (DSI) is a recent paradigm for information retrieval which uses a transformer-based neural network architecture as the document index to simplify the retrieval process. A differentiable index has many advantages enabling modifications, updates or extensions to the index. In this work, we explore balancing relevance and novel information content (diversity) for training DSI systems inspired by Maximal Marginal Relevance (MMR), and show the benefits of our approach over the naive DSI training. We present quantitative and qualitative evaluations of relevance and diversity measures obtained using our method on NQ320K and MSMARCO datasets in comparison to naive DSI. With our approach, it is possible to achieve diversity without any significant impact to relevance. Since we induce diversity while training DSI, the trained model has learned to diversify while being relevant. This obviates the need for a post-processing step to induce diversity in the recall set as typically performed using MMR. Our approach will be useful for Information Retrieval problems where both relevance and diversity are important such as in sub-topic retrieval. Our work can also be easily be extended to the incremental DSI settings which would enable fast updates to the index while retrieving a diverse recall set.