# Multiview Identifiers Enhanced Generative Retrieval

arXiv: https://arxiv.org/abs/2305.16675 · HTML: https://mlsanity.com/paper/2305.16675/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 32 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 24 of 24)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [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)
- [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://mlsanity.com/paper/2209.11755/) (2022)
- [PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them](https://mlsanity.com/paper/2102.07033/) (2021)
- [Generation-Augmented Retrieval for Open-domain Question Answering](https://mlsanity.com/paper/2009.08553/) (2020)
- [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)
- [SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval](https://mlsanity.com/paper/2207.02578/) (2022)
- [Learning Passage Impacts for Inverted Indexes](https://mlsanity.com/paper/2104.12016/) (2021)
- [Multi-View Document Representation Learning for Open-Domain Dense Retrieval](https://mlsanity.com/paper/2203.08372/) (2022)
- [Generative Multi-hop Retrieval](https://mlsanity.com/paper/2204.13596/) (2022)

## Built on by (most cited first, 16 of 16)

- [From Matching to Generation: A Survey on Generative Information Retrieval](https://mlsanity.com/paper/2404.14851/) (2024)
- [Learning to Rank in Generative Retrieval](https://mlsanity.com/paper/2306.15222/) (2023)
- [A Survey of Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.08748/) (2025)
- [A Survey of Generative Search and Recommendation in the Era of Large Language Models](https://mlsanity.com/paper/2404.16924/) (2024)
- [Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval](https://mlsanity.com/paper/2504.05181/) (2025)
- [Revolutionizing Text-to-Image Retrieval as Autoregressive Token-to-Voken Generation](https://mlsanity.com/paper/2407.17274/) (2024)
- [Exploring Training and Inference Scaling Laws in Generative Retrieval](https://mlsanity.com/paper/2503.18941/) (2025)
- [Alleviating LLM-based Generative Retrieval Hallucination in Alipay Search](https://mlsanity.com/paper/2503.21098/) (2025)
- [Scaling the Vocabulary of Non-autoregressive Models for Efficient Generative Retrieval](https://mlsanity.com/paper/2406.06739/) (2024)
- [It Takes Two to Match: Co-Evolving Generative Retriever with Reinforcement Learning](https://mlsanity.com/paper/2609.00638/) (2026)
- [Event GDR: Event-Centric Generative Document Retrieval](https://mlsanity.com/paper/2405.06886/) (2024)
- [Test-Time Scaling Strategies for Generative Retrieval in Multimodal Conversational Recommendations](https://mlsanity.com/paper/2508.18132/) (2025)
- [Autoregressive Ranking: Bridging the Gap Between Dual and Cross Encoders](https://mlsanity.com/paper/2601.05588/) (2026)
- [A Parametric Memory Head for Continual Generative Retrieval](https://mlsanity.com/paper/2604.23388/) (2026)
- [Efficient, Property-Aligned Fan-Out Retrieval via RL-Compiled Diffusion](https://mlsanity.com/paper/2603.06397/) (2026)
- [Generative Universal Multimodal Retrieval with Dual-role Identifiers](https://mlsanity.com/paper/2608.12987/) (2026)

## Abstract (arXiv)

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.