# Learning to Rank in Generative Retrieval

arXiv: https://arxiv.org/abs/2306.15222 · HTML: https://mlsanity.com/paper/2306.15222/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 34 references that are in the map.
- First posted on arXiv 27 Jun 2023.

## Builds on (most cited first, 21 of 21)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2010.08191/) (2020)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [Transformer Memory as a Differentiable Search Index](https://mlsanity.com/paper/2202.06991/) (2022)
- [Generation-Augmented Retrieval for Open-domain Question Answering](https://mlsanity.com/paper/2009.08553/) (2020)
- [BRIO: Bringing Order to Abstractive Summarization](https://mlsanity.com/paper/2203.16804/) (2022)
- [Pre-training Tasks for Embedding-based Large-scale Retrieval](https://mlsanity.com/paper/2002.03932/) (2020)
- [Autoregressive Search Engines: Generating Substrings as Document Identifiers](https://mlsanity.com/paper/2204.10628/) (2022)
- [A Neural Corpus Indexer for Document Retrieval](https://mlsanity.com/paper/2206.02743/) (2022)
- [Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)](https://mlsanity.com/paper/1405.5869/) (2014)
- [SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval](https://mlsanity.com/paper/2207.02578/) (2022)
- [How Does Generative Retrieval Scale to Millions of Passages?](https://mlsanity.com/paper/2305.11841/) (2023)
- [TOME: A Two-stage Approach for Model-based Retrieval](https://mlsanity.com/paper/2305.11161/) (2023)
- [Multiview Identifiers Enhanced Generative Retrieval](https://mlsanity.com/paper/2305.16675/) (2023)

## Built on by (most cited first, 30 of 46)

- [From Matching to Generation: A Survey on Generative Information Retrieval](https://mlsanity.com/paper/2404.14851/) (2024)
- [Generative Cross-Modal Retrieval: Memorizing Images in Multimodal Language Models for Retrieval and Beyond](https://mlsanity.com/paper/2402.10805/) (2024)
- [RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation](https://mlsanity.com/paper/2412.11919/) (2024)
- [Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decoding](https://mlsanity.com/paper/2404.14600/) (2024)
- [CorpusLM: Towards a Unified Language Model on Corpus for Knowledge-Intensive Tasks](https://mlsanity.com/paper/2402.01176/) (2024)
- [Unifying Generative and Dense Retrieval for Sequential Recommendation](https://mlsanity.com/paper/2411.18814/) (2024)
- [A Survey of Generative Search and Recommendation in the Era of Large Language Models](https://mlsanity.com/paper/2404.16924/) (2024)
- [SSRL: Self-Search Reinforcement Learning](https://mlsanity.com/paper/2508.10874/) (2025)
- [Generative Retrieval with Preference Optimization for E-commerce Search](https://mlsanity.com/paper/2407.19829/) (2024)
- [Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval](https://mlsanity.com/paper/2504.05181/) (2025)
- [Generative Retrieval as Multi-Vector Dense Retrieval](https://mlsanity.com/paper/2404.00684/) (2024)
- [Permutative Preference Alignment from Listwise Ranking of Human Judgments](https://mlsanity.com/paper/2410.04346/) (2024)
- [Preference Discerning with LLM-Enhanced Generative Retrieval](https://mlsanity.com/paper/2412.08604/) (2024)
- [From Ranking to Selection: A Simple but Efficient Dynamic Passage Selector for Retrieval Augmented Generation](https://mlsanity.com/paper/2508.09497/) (2025)
- [On Synthetic Data Strategies for Domain-Specific Generative Retrieval](https://mlsanity.com/paper/2502.17957/) (2025)
- [LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements Generation](https://mlsanity.com/paper/2505.23832/) (2025)
- [A Survey of Generative Information Retrieval](https://mlsanity.com/paper/2406.01197/) (2024)
- [MixLoRA-DSI: Dynamically Expandable Mixture-of-LoRA Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora](https://mlsanity.com/paper/2507.09924/) (2025)
- [LLM-guided Hierarchical Search for End-to-end Reasoning Intensive Retrieval](https://mlsanity.com/paper/2510.13217/) (2025)
- [Bottleneck-Minimal Indexing for Generative Document Retrieval](https://mlsanity.com/paper/2405.10974/) (2024)
- [Learning to Rank Salient Content for Query-focused Summarization](https://mlsanity.com/paper/2411.00324/) (2024)
- [Multi-Turn Multi-Modal Question Clarification for Enhanced Conversational Understanding](https://mlsanity.com/paper/2502.11442/) (2025)
- [DiffuGR: Generative Document Retrieval with Diffusion Language Models](https://mlsanity.com/paper/2511.08150/) (2025)
- [Distillation Enhanced Generative Retrieval](https://mlsanity.com/paper/2402.10769/) (2024)
- [Scaling the Vocabulary of Non-autoregressive Models for Efficient Generative Retrieval](https://mlsanity.com/paper/2406.06739/) (2024)
- [Advances in Artificial Intelligence: A Review for the Creative Industries](https://mlsanity.com/paper/2501.02725/) (2025)
- [It Takes Two to Match: Co-Evolving Generative Retriever with Reinforcement Learning](https://mlsanity.com/paper/2609.00638/) (2026)
- [RAVEN: Rethinking Adversarial Video Generation with Efficient Tri-plane Networks](https://mlsanity.com/paper/2401.06035/) (2024)
- [Baby Bear: Seeking a Just Right Rating Scale for Scalar Annotations](https://mlsanity.com/paper/2408.09765/) (2024)
- [Neural Corrective Machine Unranking](https://mlsanity.com/paper/2411.08562/) (2024)

## Abstract (arXiv)

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.