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

arXiv: https://arxiv.org/abs/2311.18550 · HTML: https://mlsanity.com/paper/2311.18550/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://mlsanity.com/paper/2311.16452/) (2023)
- [Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?](https://mlsanity.com/paper/2302.03495/) (2023)
- [Dr ChatGPT, tell me what I want to hear: How prompt knowledge impacts health answer correctness](https://mlsanity.com/paper/2302.13793/) (2023)

## Built on by (most cited first, 17 of 17)

- [Benchmarking Retrieval-Augmented Generation for Medicine](https://mlsanity.com/paper/2402.13178/) (2024)
- [From Matching to Generation: A Survey on Generative Information Retrieval](https://mlsanity.com/paper/2404.14851/) (2024)
- [Supervising the search process produces reliable and generalizable information-seeking agents](https://mlsanity.com/paper/2502.13957/) (2025)
- [On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains](https://mlsanity.com/paper/2409.17275/) (2024)
- [Evaluating Search Engines and Large Language Models for Answering Health Questions](https://mlsanity.com/paper/2407.12468/) (2024)
- [MRAG: Benchmarking Retrieval-Augmented Generation for Bio-medicine](https://mlsanity.com/paper/2601.16503/) (2026)
- [Sports and Women's Sports: Gender Bias in Text Generation with Olympic Data](https://mlsanity.com/paper/2502.04218/) (2025)
- [MedTrust-RAG: Evidence Verification and Trust Alignment for Biomedical Question Answering](https://mlsanity.com/paper/2510.14400/) (2025)
- [The Epistemological Consequences of Large Language Models: Rethinking collective intelligence and institutional knowledge](https://mlsanity.com/paper/2512.19570/) (2025)
- [Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution](https://mlsanity.com/paper/2603.05308/) (2026)
- [Generative AI in Medicine](https://mlsanity.com/paper/2412.10337/) (2024)
- [Evaluating ChatGPT on Medical Information Extraction Tasks: Performance, Explainability and Beyond](https://mlsanity.com/paper/2601.21767/) (2026)
- [Pursuing Best Industrial Practices for Retrieval-Augmented Generation in the Medical Domain](https://mlsanity.com/paper/2602.03368/) (2026)
- [ACL: Aligned Contrastive Learning Improves BERT and Multi-exit BERT Fine-tuning](https://mlsanity.com/paper/2602.03563/) (2026)
- [Towards Zero-Shot, Controllable Dialog Planning with LLMs](https://mlsanity.com/paper/2410.05821/) (2024)
- [A Generative Model for Joint Multiple Intent Detection and Slot Filling](https://mlsanity.com/paper/2602.08322/) (2026)
- [Generalization Is Stability, Not Accuracy: Multi-Axis Evaluation of LLMs](https://mlsanity.com/paper/2610.01428/) (2026)

## 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.