Instead of simply matching a query to pre-existing passages, generative
retrieval generates identifier strings of passages as the retrieval target. At
a cost, the identifier must be distinctive enough to represent a passage.
Current approaches use either a numeric ID or a text piece (such as a title or
substrings) as the identifier. However, these identifiers cannot cover a
passage's content well. As such, we are motivated to propose a new type of
identifier, synthetic identifiers, that are generated based on the content of a
passage and could integrate contextualized information that text pieces lack.
Furthermore, we simultaneously consider multiview identifiers, including
synthetic identifiers, titles, and substrings. These views of identifiers
complement each other and facilitate the holistic ranking of passages from
multiple perspectives. We conduct a series of experiments on three public
datasets, and the results indicate that our proposed approach performs the best
in generative retrieval, demonstrating its effectiveness and robustness.

本研究提出了一种新的生成式检索方法，使用合成标识符来代替传统的数字 ID 或文本片段标识符。同时，多视图标识符的综合排名可以更好地匹配查询 - 检索。在三个公共数据集上进行的实验证明了这种方法的高效性和稳定性。