mlsanityData to 7 Oct 2026

Search Still Matters: Information Retrieval in the Era of Generative AI

arXiv 2311.18550 ↗First posted 30 Nov 2023cs.IR cs.AIcited by 17

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Abstract (arXiv)

Objective: Information retrieval (IR, also known as search) systems are ubiquitous in modern times. How does the emergence of generative artificial intelligence (AI), based on large language models (LLMs), fit into the IR process? Process: This perspective explores the use of generative AI in the context of the motivations, considerations, and outcomes of the IR process with a focus on the academic use of such systems. Conclusions: There are many information needs, from simple to complex, that motivate use of IR. Users of such systems, particularly academics, have concerns for authoritativeness, timeliness, and contextualization of search. While LLMs may provide functionality that aids the IR process, the continued need for search systems, and research into their improvement, remains essential.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 5 of its 18 references that are in the map. First posted on arXiv 30 Nov 2023.

Builds on

5 of 18 references.

References · 5

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 663
  3. 2023Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicinecited by 319
  4. 2023Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?cited by 54
  5. 2023Dr ChatGPT, tell me what I want to hear: How prompt knowledge impacts health answer correctnesscited by 15

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 4; 2026: 7.

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  1. 2024Benchmarking Retrieval-Augmented Generation for Medicinecited by 326
  2. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  3. 2025Supervising the search process produces reliable and generalizable information-seeking agentscited by 20
  4. 2024On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domainscited by 14
  5. 2024Evaluating Search Engines and Large Language Models for Answering Health Questionscited by 5
  6. 2026MRAG: Benchmarking Retrieval-Augmented Generation for Bio-medicinecited by 4
  7. 2025Sports and Women's Sports: Gender Bias in Text Generation with Olympic Datacited by 3
  8. 2025The Epistemological Consequences of Large Language Models: Rethinking collective intelligence and institutional knowledgecited by 2
  9. 2026Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attributioncited by 2
  10. 2024Generative AI in Medicinecited by 1
  11. 2025MedTrust-RAG: Evidence Verification and Trust Alignment for Biomedical Question Answeringcited by 1
  12. 2026Evaluating ChatGPT on Medical Information Extraction Tasks: Performance, Explainability and Beyondcited by 1
5 more
  1. 2026Pursuing Best Industrial Practices for Retrieval-Augmented Generation in the Medical Domaincited by 1
  2. 2026ACL: Aligned Contrastive Learning Improves BERT and Multi-exit BERT Fine-tuningcited by 1
  3. 2024Towards Zero-Shot, Controllable Dialog Planning with LLMscited by 0
  4. 2026A Generative Model for Joint Multiple Intent Detection and Slot Fillingcited by 0
  5. 2026Generalization Is Stability, Not Accuracy: Multi-Axis Evaluation of LLMscited by 0