mlsanityData to 7 Oct 2026

Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community

arXiv 2307.09751 ↗First posted 19 Jul 2023cs.IR cs.AIcited by 29

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Abstract (arXiv)

The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Models (LLMs) have demonstrated exceptional capabilities in text understanding, generation, and knowledge inference, opening up exciting avenues for IR research. LLMs not only facilitate generative retrieval but also offer improved solutions for user understanding, model evaluation, and user-system interactions. More importantly, the synergistic relationship among IR models, LLMs, and humans forms a new technical paradigm that is more powerful for information seeking. IR models provide real-time and relevant information, LLMs contribute internal knowledge, and humans play a central role of demanders and evaluators to the reliability of information services. Nevertheless, significant challenges exist, including computational costs, credibility concerns, domain-specific limitations, and ethical considerations. To thoroughly discuss the transformative impact of LLMs on IR research, the Chinese IR community conducted a strategic workshop in April 2023, yielding valuable insights. This paper provides a summary of the workshop's outcomes, including the rethinking of IR's core values, the mutual enhancement of LLMs and IR, the proposal of a novel IR technical paradigm, and open challenges.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 63 of its 126 references that are in the map. First posted on arXiv 19 Jul 2023.

Builds on

63 of 126 references.

References · 63

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Citing papers per year: 2023: 5; 2024: 13; 2025: 9; 2026: 2.

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  1. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  2. 2024Chain of Agents: Large Language Models Collaborating on Long-Context Taskscited by 201
  3. 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Eracited by 97
  4. 2024IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologuescited by 40
  5. 2023Neural Retrievers are Biased Towards LLM-Generated Contentcited by 37
  6. 2023Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomiescited by 34
  7. 2024AI Hallucinations: A Misnomer Worth Clarifyingcited by 34
  8. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrievalcited by 31
  9. 2024A Review of Multi-Modal Large Language and Vision Modelscited by 30
  10. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  11. 2024Cocktail: A Comprehensive Information Retrieval Benchmark with LLM-Generated Documents Integrationcited by 17
  12. 2023Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Imagescited by 13
17 more
  1. 2024Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Frameworkcited by 12
  2. 2025QE-RAG: A Robust Retrieval-Augmented Generation Benchmark for Query Entry Errorscited by 10
  3. 2025Mask-GCG: Are All Tokens in Adversarial Suffixes Necessary for Jailbreak Attacks?cited by 6
  4. 2024Retrieval Augmented Thought Process for Private Data Handling in Healthcarecited by 5
  5. 2025CoRank: LLM-Based Compact Reranking with Document Features for Scientific Retrievalcited by 5
  6. 2025Characterizing Web Search in The Age of Generative AIcited by 5
  7. 2024Self-Retrieval: End-to-End Information Retrieval with One Large Language Modelcited by 4
  8. 2024Optimizing RAG Techniques for Automotive Industry PDF Chatbots: A Case Study with Locally Deployed Ollama Modelscited by 4
  9. 2023LLMs Perform Poorly at Concept Extraction in Cyber-security Research Literaturecited by 3
  10. 2024Overcoming LLM Challenges using RAG-Driven Precision in Coffee Leaf Disease Remediationcited by 3
  11. 2025PerFairX: Is There a Balance Between Fairness and Personality in Large Language Model Recommendations?cited by 3
  12. 2024PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Predictioncited by 2
  13. 2025Foundations of GenIRcited by 1
  14. 2025A GenAI System for Improved FAIR Independent Biological Database Integrationcited by 1
  15. 2025Sherlock Your Queries: Learning to Ask the Right Questions for Dialogue-Based Retrievalcited by 1
  16. 2026Analytical Searchcited by 1
  17. 2026Supporting software engineering tasks with agentic AI: Demonstration on document retrieval and test scenario generationcited by 0