# Scalable and Effective Generative Information Retrieval

arXiv: https://arxiv.org/abs/2311.09134 · HTML: https://mlsanity.com/paper/2311.09134/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2023.

## Built on by (most cited first, 30 of 39)

- [From Matching to Generation: A Survey on Generative Information Retrieval](https://mlsanity.com/paper/2404.14851/) (2024)
- [Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond](https://mlsanity.com/paper/2403.10667/) (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)
- [Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrieval](https://mlsanity.com/paper/2504.01403/) (2025)
- [Retrieval-Enhanced Machine Learning: Synthesis and Opportunities](https://mlsanity.com/paper/2407.12982/) (2024)
- [Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval](https://mlsanity.com/paper/2504.05181/) (2025)
- [Beyond Memorization: The Challenge of Random Memory Access in Language Models](https://mlsanity.com/paper/2403.07805/) (2024)
- [Generative Retrieval as Multi-Vector Dense Retrieval](https://mlsanity.com/paper/2404.00684/) (2024)
- [Revolutionizing Text-to-Image Retrieval as Autoregressive Token-to-Voken Generation](https://mlsanity.com/paper/2407.17274/) (2024)
- [LawPal : A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India](https://mlsanity.com/paper/2502.16573/) (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)
- [Exploring Training and Inference Scaling Laws in Generative Retrieval](https://mlsanity.com/paper/2503.18941/) (2025)
- [MixLoRA-DSI: Dynamically Expandable Mixture-of-LoRA Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora](https://mlsanity.com/paper/2507.09924/) (2025)
- [Bottleneck-Minimal Indexing for Generative Document Retrieval](https://mlsanity.com/paper/2405.10974/) (2024)
- [Generative Retrieval with Few-shot Indexing](https://mlsanity.com/paper/2408.02152/) (2024)
- [MultiRAG: A Knowledge-guided Framework for Mitigating Hallucination in Multi-source Retrieval Augmented Generation](https://mlsanity.com/paper/2508.03553/) (2025)
- [DiffuGR: Generative Document Retrieval with Diffusion Language Models](https://mlsanity.com/paper/2511.08150/) (2025)
- [Scaling the Vocabulary of Non-autoregressive Models for Efficient Generative Retrieval](https://mlsanity.com/paper/2406.06739/) (2024)
- [Bootstrapped Pre-training with Dynamic Identifier Prediction for Generative Retrieval](https://mlsanity.com/paper/2407.11504/) (2024)
- [Aug2Search: Enhancing Facebook Marketplace Search with LLM-Generated Synthetic Data Augmentation](https://mlsanity.com/paper/2505.16065/) (2025)
- [Multimodal Data Storage and Retrieval for Embodied AI: A Survey](https://mlsanity.com/paper/2508.13901/) (2025)
- [CFVBench: A Comprehensive Video Benchmark for Fine-grained Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2510.09266/) (2025)
- [Aligning Large Language Models with Searcher Preferences](https://mlsanity.com/paper/2603.10473/) (2026)
- [It Takes Two to Match: Co-Evolving Generative Retriever with Reinforcement Learning](https://mlsanity.com/paper/2609.00638/) (2026)
- [PromptDSI: Prompt-based Rehearsal-free Continual Learning for Document Retrieval](https://mlsanity.com/paper/2406.12593/) (2024)
- [Foundations of GenIR](https://mlsanity.com/paper/2501.02842/) (2025)
- [Retrieval-in-the-Chain: Bootstrapping Large Language Models for Generative Retrieval](https://mlsanity.com/paper/2510.13095/) (2025)
- [BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models](https://mlsanity.com/paper/2601.22925/) (2026)

## Abstract (arXiv)

Recent research has shown that transformer networks can be used as differentiable search indexes by representing each document as a sequences of document ID tokens. These generative retrieval models cast the retrieval problem to a document ID generation problem for each given query. Despite their elegant design, existing generative retrieval models only perform well on artificially-constructed and small-scale collections. This has led to serious skepticism in the research community on their real-world impact. This paper represents an important milestone in generative retrieval research by showing, for the first time, that generative retrieval models can be trained to perform effectively on large-scale standard retrieval benchmarks. For doing so, we propose RIPOR- an optimization framework for generative retrieval that can be adopted by any encoder-decoder architecture. RIPOR is designed based on two often-overlooked fundamental design considerations in generative retrieval. First, given the sequential decoding nature of document ID generation, assigning accurate relevance scores to documents based on the whole document ID sequence is not sufficient. To address this issue, RIPOR introduces a novel prefix-oriented ranking optimization algorithm. Second, initial document IDs should be constructed based on relevance associations between queries and documents, instead of the syntactic and semantic information in the documents. RIPOR addresses this issue using a relevance-based document ID construction approach that quantizes relevance-based representations learned for documents. Evaluation on MSMARCO and TREC Deep Learning Track reveals that RIPOR surpasses state-of-the-art generative retrieval models by a large margin (e.g., 30.5% MRR improvements on MS MARCO Dev Set), and perform better on par with popular dense retrieval models.