Query Expansion in the Age of Pre-trained and Large Language Models: A Comprehensive Survey

• URL: http://arxiv.org/abs/2509.07794v2
• Date: Sat, 25 Oct 2025 13:13:22 GMT
• Title: Query Expansion in the Age of Pre-trained and Large Language Models: A Comprehensive Survey
• Authors: Minghan Li, Xinxuan Lv, Junjie Zou, Tongna Chen, Chao Zhang, Suchao An, Ercong Nie, Guodong Zhou,
• Abstract summary: Modern information retrieval must reconcile ambiguous queries with diverse and dynamic corpora.<n>We organize recent work along four complementary dimensions: the point of injection, grounding and interaction, learning and alignment, and knowledge-graph integration.<n>The survey compares traditional and neural QE across seven aspects and maps applications in web search, biomedicine, e-commerce, open-domain question answering/RAG, conversational and code search, and cross-lingual settings.
• Score: 21.764997953030857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern information retrieval (IR) must reconcile short, ambiguous queries with increasingly diverse and dynamic corpora. Query expansion (QE) remains central to alleviating vocabulary mismatch, yet the design space has shifted with pre-trained and large language models (PLMs, LLMs). In this survey, we organize recent work along four complementary dimensions: the point of injection (implicit/embedding vs. selection-based explicit), grounding and interaction (from zero-grounding prompts to multi-round retrieve-expand loops), learning and alignment (SFT/PEFT/DPO), and knowledge-graph integration. A model-centric taxonomy is also outlined, spanning encoder-only, encoder-decoder, decoder-only, instruction-tuned, and domain or multilingual variants, with affordances for QE such as contextual disambiguation, controllable generation, and zero-shot or few-shot reasoning. Practice-oriented guidance specifies where neural QE helps most: first-stage retrieval, multi-query fusion, re-ranking, and retrieval-augmented generation (RAG). The survey compares traditional and neural QE across seven aspects and maps applications in web search, biomedicine, e-commerce, open-domain question answering/RAG, conversational and code search, and cross-lingual settings. The survey concludes with an agenda focused on reliable, safe, efficient, and adaptive QE, offering a principled blueprint for deploying and combining techniques under real-world constraints.

Related papers

• LEAPS: An LLM-Empowered Adaptive Plugin for Taobao AI Search [17.074638179635613]
  We propose LEAPS (LLM-Empowered Adaptive for Taobao AI Search), which seamlessly upgrades traditional search systems via a "Broaden-and-Empower" paradigm.<n>It employs a novel three-stage training strategy--inverse data augmentation, posterior-knowledge supervised fine-tuning, and diversity-aware reinforcement learning--to generate adaptive and complementary query combinations.<n>Fully deployed on Taobao AI Search since August 2025, LEAPS currently serves hundreds of millions of users monthly.
  arXiv  Detail & Related papers  (2026-01-09T03:41:27Z)
• Tagging-Augmented Generation: Assisting Language Models in Finding Intricate Knowledge In Long Contexts [6.14987671066697]
  In this paper, we propose Tagging-Augmented Generation (TAG) as a lightweight data augmentation strategy for long-context scenarios.<n>We validate our hypothesis by augmenting two challenging and directly relevant question-answering benchmarks -- NoLima and NovelQA.<n>We show that tagging the context or even just adding tag definitions into QA prompts leads to consistent performance gains over the baseline.
  arXiv  Detail & Related papers  (2025-10-27T03:23:25Z)
• The benefits of query-based KGQA systems for complex and temporal questions in LLM era [55.20230501807337]
  Large language models excel in question-answering (QA) yet still struggle with multi-hop reasoning and temporal questions.<n> Query-based knowledge graph QA (KGQA) offers a modular alternative by generating executable queries instead of direct answers.<n>We explore multi-stage query-based framework for WikiData QA, proposing multi-stage approach that enhances performance on challenging multi-hop and temporal benchmarks.
  arXiv  Detail & Related papers  (2025-07-16T06:41:03Z)
• HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation [9.175609521889266]
  We introduce our new RAG instruction fine-tuning method, Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation (HIRAG)<n>This method enhances the model's open-book examination capability by utilizing multi-level progressive chain-of-thought.<n>Experiments show that the HIRAG training strategy significantly improves the model's performance on datasets such as RGB, PopQA, MuSiQue, HotpotQA, and PubmedQA.
  arXiv  Detail & Related papers  (2025-07-08T06:53:28Z)
• SQuARE: Sequential Question Answering Reasoning Engine for Enhanced Chain-of-Thought in Large Language Models [4.328173053224842]
  This paper introduces SQuARE, a novel prompting technique designed to improve reasoning through a self-interrogation paradigm.<n>Building upon CoT frameworks, SQuARE prompts models to generate and resolve multiple auxiliary questions before tackling the main query.<n>Our evaluations, conducted with Llama 3 and GPT-4o models across multiple question-answering datasets, demonstrate that SQuARE significantly surpasses traditional CoT prompts and existing rephrase-and-respond methods.
  arXiv  Detail & Related papers  (2025-02-13T15:07:20Z)
• Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent [92.5712549836791]
  Multimodal Retrieval Augmented Generation (mRAG) plays an important role in mitigating the "hallucination" issue inherent in multimodal large language models (MLLMs)<n>We propose the first self-adaptive planning agent for multimodal retrieval, OmniSearch.
  arXiv  Detail & Related papers  (2024-11-05T09:27:21Z)
• Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations [3.3018718917393297]
  We propose the novel task of Question Answering with source citation in ambiguous settings, where multiple valid answers exist. We create a comprehensive framework consisting of: (1) five novel datasets; (2) the first ambiguous multi-hop QA dataset featuring real-world, naturally occurring contexts; and (3) two new metrics to evaluate models' performances. We hope that this new task, datasets, metrics, and baselines will inspire the community to push the boundaries of QA research and develop more trustworthy and interpretable systems.
  arXiv  Detail & Related papers  (2024-10-05T17:37:01Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• Large Language Models for Information Retrieval: A Survey [58.30439850203101]
  Information retrieval has evolved from term-based methods to its integration with advanced neural models. Recent research has sought to leverage large language models (LLMs) to improve IR systems. We delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers.
  arXiv  Detail & Related papers  (2023-08-14T12:47:22Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)
• Recitation-Augmented Language Models [85.30591349383849]
  We show that RECITE is a powerful paradigm for knowledge-intensive NLP tasks. Specifically, we show that by utilizing recitation as the intermediate step, a recite-and-answer scheme can achieve new state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-10-04T00:49:20Z)
• QASem Parsing: Text-to-text Modeling of QA-based Semantics [19.42681342441062]
  We consider three QA-based semantic tasks, namely, QA-SRL, QANom and QADiscourse. We release the first unified QASem parsing tool, practical for downstream applications.
  arXiv  Detail & Related papers  (2022-05-23T15:56:07Z)
• Conversations with Search Engines: SERP-based Conversational Response Generation [77.1381159789032]
  We create a suitable dataset, the Search as a Conversation (SaaC) dataset, for the development of pipelines for conversations with search engines. We also develop a state-of-the-art pipeline for conversations with search engines, the Conversations with Search Engines (CaSE) using this dataset. CaSE enhances the state-of-the-art by introducing a supporting token identification module and aprior-aware pointer generator.
  arXiv  Detail & Related papers  (2020-04-29T13:07:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.