mlsanityData to 7 Oct 2026

Query Understanding in the Age of Large Language Models

arXiv 2306.16004 ↗First posted 28 Jun 2023cs.IR cs.AIcited by 7

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Abstract (arXiv)

Querying, conversing, and controlling search and information-seeking interfaces using natural language are fast becoming ubiquitous with the rise and adoption of large-language models (LLM). In this position paper, we describe a generic framework for interactive query-rewriting using LLMs. Our proposal aims to unfold new opportunities for improved and transparent intent understanding while building high-performance retrieval systems using LLMs. A key aspect of our framework is the ability of the rewriter to fully specify the machine intent by the search engine in natural language that can be further refined, controlled, and edited before the final retrieval phase. The ability to present, interact, and reason over the underlying machine intent in natural language has profound implications on transparency, ranking performance, and a departure from the traditional way in which supervised signals were collected for understanding intents. We detail the concept, backed by initial experiments, along with open questions for this interactive query understanding framework.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 55 of its 96 references that are in the map. First posted on arXiv 28 Jun 2023.

Builds on

55 of 96 references.

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Citing papers per year: 2023: 1; 2024: 3; 2025: 3.

7 papers.

  1. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
  2. 2023Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLMcited by 18
  3. 2024EXPLORA: Efficient Exemplar Subset Selection for Complex Reasoningcited by 5
  4. 2025Examples as the Prompt: A Scalable Approach for Efficient LLM Adaptation in E-Commercecited by 1
  5. 2025Standard Applicability Judgment and Cross-jurisdictional Reasoning: A RAG-based Framework for Medical Device Compliancecited by 1
  6. 2024From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical Regulatory Compliance Processcited by 0
  7. 2025Dagstuhl Perspectives Workshop 24352 -- Conversational Agents: A Framework for Evaluation (CAFE): Manifestocited by 0