# Doc2Query--: When Less is More

arXiv: https://arxiv.org/abs/2301.03266 · HTML: https://mlsanity.com/paper/2301.03266/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2023.

## Built on by (most cited first, 21 of 21)

- [GME: Improving Universal Multimodal Retrieval by Multimodal LLMs](https://mlsanity.com/paper/2412.16855/) (2024)
- [How Does Generative Retrieval Scale to Millions of Passages?](https://mlsanity.com/paper/2305.11841/) (2023)
- [Context Aware Query Rewriting for Text Rankers using LLM](https://mlsanity.com/paper/2308.16753/) (2023)
- [DAPR: A Benchmark on Document-Aware Passage Retrieval](https://mlsanity.com/paper/2305.13915/) (2023)
- [Metadata-Driven Retrieval-Augmented Generation for Financial Question Answering](https://mlsanity.com/paper/2510.24402/) (2025)
- [Revela: Dense Retriever Learning via Language Modeling](https://mlsanity.com/paper/2506.16552/) (2025)
- [GRAIL: A Deep-Granularity Hybrid Resonance Framework for Real-Time Agent Discovery via SLM-Enhanced Indexing](https://mlsanity.com/paper/2605.02489/) (2026)
- [RL-Index: Reinforcement Learning for Retrieval Index Reasoning](https://mlsanity.com/paper/2606.16316/) (2026)
- [SoftQE: Learned Representations of Queries Expanded by LLMs](https://mlsanity.com/paper/2402.12663/) (2024)
- [Query-Centric Graph Retrieval Augmented Generation](https://mlsanity.com/paper/2509.21237/) (2025)
- [With Argus Eyes: Assessing Retrieval Gaps via Uncertainty Scoring to Detect and Remedy Retrieval Blind Spots](https://mlsanity.com/paper/2602.09616/) (2026)
- [ReFormeR: Learning and Applying Explicit Query Reformulation Patterns](https://mlsanity.com/paper/2604.01417/) (2026)
- [Document Optimization for Black-Box Retrieval via Reinforcement Learning](https://mlsanity.com/paper/2604.05087/) (2026)
- [Bridging the Question-Answer Gap in Retrieval-Augmented Generation: Hypothetical Prompt Embeddings](https://mlsanity.com/paper/2607.29402/) (2026)
- [LargePiG: Your Large Language Model is Secretly a Pointer Generator](https://mlsanity.com/paper/2410.11366/) (2024)
- [Chunk Knowledge Generation Model for Enhanced Information Retrieval: A Multi-task Learning Approach](https://mlsanity.com/paper/2509.15658/) (2025)
- [RAQG-QPP: Query Performance Prediction with Retrieved Query Variants and Retrieval Augmented Query Generation](https://mlsanity.com/paper/2604.27244/) (2026)
- [E-MIA: Exam-Style Black-Box Membership Inference Attacks against RAG Systems](https://mlsanity.com/paper/2605.00955/) (2026)
- [AutoIndex: Learning Representation Programs for Retrieval](https://mlsanity.com/paper/2607.18603/) (2026)
- [Self-Evolving Search Index](https://mlsanity.com/paper/2609.19656/) (2026)
- [Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion](https://mlsanity.com/paper/2610.13810/) (2026)

## Abstract (arXiv)

Doc2Query -- the process of expanding the content of a document before indexing using a sequence-to-sequence model -- has emerged as a prominent technique for improving the first-stage retrieval effectiveness of search engines. However, sequence-to-sequence models are known to be prone to "hallucinating" content that is not present in the source text. We argue that Doc2Query is indeed prone to hallucination, which ultimately harms retrieval effectiveness and inflates the index size. In this work, we explore techniques for filtering out these harmful queries prior to indexing. We find that using a relevance model to remove poor-quality queries can improve the retrieval effectiveness of Doc2Query by up to 16%, while simultaneously reducing mean query execution time by 23% and cutting the index size by 33%. We release the code, data, and a live demonstration to facilitate reproduction and further exploration at https://github.com/terrierteam/pyterrier_doc2query.