Learning to Rank in Generative Retrieval

Authors

  • Yongqi Li The Hong Kong Polytechnic University
  • Nan Yang Microsoft
  • Liang Wang Microsoft
  • Furu Wei Microsoft
  • Wenjie Li The Hong Kong Polytechnic University

DOI:

https://doi.org/10.1609/aaai.v38i8.28717

Keywords:

DMKM: Knowledge Acquisition from the Web, NLP: Question Answering

Abstract

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.

Published

2024-03-24

How to Cite

Li, Y., Yang, N., Wang, L., Wei, F., & Li, W. (2024). Learning to Rank in Generative Retrieval. Proceedings of the AAAI Conference on Artificial Intelligence, 38(8), 8716-8723. https://doi.org/10.1609/aaai.v38i8.28717

Issue

Section

AAAI Technical Track on Data Mining & Knowledge Management