Generative Retrieval for Long Sequences

Text retrieval is often formulated as mapping the query and the target items (e.g., passages) to the same vector space and finding the item whose embedding is closest to that of the query. In this paper, we explore a generative approach as an alternative, where we use an encoder-decoder model to memorize the target corpus in a generative manner and then finetune it on query-to-passage generation. As GENRE(Cao et al., 2021) has shown that entities can be retrieved in a generative way, our work can be considered as its generalization to longer text. We show that it consistently achieves comparable performance to traditional bi-encoder retrieval on diverse datasets and is especially strong at retrieving highly structured items, such as reasoning chains and graph relations, while demonstrating superior GPU memory and time complexity. We also conjecture that generative retrieval is complementary to traditional retrieval, as we find that an ensemble of both outperforms homogeneous ensembles.