# Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?

arXiv: https://arxiv.org/abs/2311.09175 · HTML: https://mlsanity.com/paper/2311.09175/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 46 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Builds on (most cited first, 20 of 20)

- [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)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://mlsanity.com/paper/2212.10496/) (2022)
- [Document Ranking with a Pretrained Sequence-to-Sequence Model](https://mlsanity.com/paper/2003.06713/) (2020)
- [Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://mlsanity.com/paper/2304.09542/) (2023)
- [Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting](https://mlsanity.com/paper/2306.17563/) (2023)
- [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://mlsanity.com/paper/2209.11755/) (2022)
- [Overview of the TREC 2020 deep learning track](https://mlsanity.com/paper/2102.07662/) (2021)
- [Query2doc: Query Expansion with Large Language Models](https://mlsanity.com/paper/2303.07678/) (2023)
- [A Deep Relevance Matching Model for Ad-hoc Retrieval](https://mlsanity.com/paper/1711.08611/) (2017)
- [CEDR: Contextualized Embeddings for Document Ranking](https://mlsanity.com/paper/1904.07094/) (2019)
- [Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls](https://mlsanity.com/paper/2108.11044/) (2021)
- [When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets](https://mlsanity.com/paper/2309.08541/) (2023)
- [Generative Query Reformulation for Effective Adhoc Search](https://mlsanity.com/paper/2308.00415/) (2023)
- [Using Word Embeddings for Automatic Query Expansion](https://mlsanity.com/paper/1606.07608/) (2016)
- [Deep Neural Networks for Query Expansion using Word Embeddings](https://mlsanity.com/paper/1811.03514/) (2018)
- [Improving Out-of-Distribution Generalization of Neural Rerankers with Contextualized Late Interaction](https://mlsanity.com/paper/2302.06589/) (2023)

## Built on by (most cited first, 12 of 12)

- [Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs](https://mlsanity.com/paper/2507.09477/) (2025)
- [ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance](https://mlsanity.com/paper/2502.17057/) (2025)
- [CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation](https://mlsanity.com/paper/2410.23090/) (2024)
- [Drowning in Documents: Consequences of Scaling Reranker Inference](https://mlsanity.com/paper/2411.11767/) (2024)
- [Large Language Models as Zero-Shot Keyphrase Extractors: A Preliminary Empirical Study](https://mlsanity.com/paper/2312.15156/) (2023)
- [Beyond Contrastive Learning: Synthetic Data Enables List-wise Training with Multiple Levels of Relevance](https://mlsanity.com/paper/2503.23239/) (2025)
- [Investigating Task Arithmetic for Zero-Shot Information Retrieval](https://mlsanity.com/paper/2505.00649/) (2025)
- [E2Rank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Search](https://mlsanity.com/paper/2510.22733/) (2025)
- [GenCRF: Generative Clustering and Reformulation Framework for Enhanced Intent-Driven Information Retrieval](https://mlsanity.com/paper/2409.10909/) (2024)
- [QueStER: Query Specification for Generative keyword-based Retrieval](https://mlsanity.com/paper/2511.05301/) (2025)
- [Completing Missing Annotation: Multi-Agent Debate for Accurate and Scalable Relevant Assessment for IR Benchmarks](https://mlsanity.com/paper/2602.06526/) (2026)
- [SkillSeek: Revisiting Agent Skill Retrieval at Marketplace Scale](https://mlsanity.com/paper/2609.38822/) (2026)

## Abstract (arXiv)

Query expansion has been widely used to improve the search results of first-stage retrievers, yet its influence on second-stage, cross-encoder rankers remains under-explored. A recent work of Weller et al. [44] shows that current expansion techniques benefit weaker models such as DPR and BM25 but harm stronger rankers such as MonoT5. In this paper, we re-examine this conclusion and raise the following question: Can query expansion improve generalization of strong cross-encoder rankers? To answer this question, we first apply popular query expansion methods to state-of-the-art cross-encoder rankers and verify the deteriorated zero-shot performance. We identify two vital steps for cross-encoders in the experiment: high-quality keyword generation and minimal-disruptive query modification. We show that it is possible to improve the generalization of a strong neural ranker, by prompt engineering and aggregating the ranking results of each expanded query via fusion. Specifically, we first call an instruction-following language model to generate keywords through a reasoning chain. Leveraging self-consistency and reciprocal rank weighting, we further combine the ranking results of each expanded query dynamically. Experiments on BEIR and TREC Deep Learning 2019/2020 show that the nDCG@10 scores of both MonoT5 and RankT5 following these steps are improved, which points out a direction for applying query expansion to strong cross-encoder rankers.