  Generative retrieval stands out as a promising new paradigm in text retrieval
that aims to generate identifier strings of relevant passages as the retrieval
target. This generative paradigm taps into powerful generative language models,
distinct from traditional sparse or dense retrieval methods. However, only
learning to generate is insufficient for generative retrieval. Generative
retrieval learns to generate identifiers of relevant passages as an
intermediate goal and then converts predicted identifiers into the final
passage rank list. The disconnect between the learning objective of
autoregressive models and the desired passage ranking target leads to a
learning gap. To bridge this gap, we propose a learning-to-rank framework for
generative retrieval, dubbed LTRGR. LTRGR enables generative retrieval to learn
to rank passages directly, optimizing the autoregressive model toward the final
passage ranking target via a rank loss. This framework only requires an
additional learning-to-rank training phase to enhance current generative
retrieval systems and does not add any burden to the inference stage. We
conducted experiments on three public benchmarks, and the results demonstrate
that LTRGR achieves state-of-the-art performance among generative retrieval
methods. The code and checkpoints are released at
https://github.com/liyongqi67/LTRGR.
