Multiview Identifiers Enhanced Generative Retrieval
Yongqi Li Nan Yang Liang Wang Furu Wei Wenjie Li
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.