mlsanityData to 14 Oct 2026

The Use of Generative Search Engines for Knowledge Work and Complex Tasks

arXiv 2404.04268 ↗First posted 19 Mar 2024cs.IR cs.AI cs.CYcited by 18

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Abstract (arXiv)

Until recently, search engines were the predominant method for people to access online information. The recent emergence of large language models (LLMs) has given machines new capabilities such as the ability to generate new digital artifacts like text, images, code etc., resulting in a new tool, a generative search engine, which combines the capabilities of LLMs with a traditional search engine. Through the empirical analysis of Bing Copilot (Bing Chat), one of the first publicly available generative search engines, we analyze the types and complexity of tasks that people use Bing Copilot for compared to Bing Search. Findings indicate that people use the generative search engine for more knowledge work tasks that are higher in cognitive complexity than were commonly done with a traditional search engine.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 6 of its 32 references that are in the map. First posted on arXiv 19 Mar 2024.

Builds on

6 of 32 references.

References · 6

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  4. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 274
  5. 2023GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Modelscited by 176
  6. 2023Generative artificial intelligence enhances creativity but reduces the diversity of novel contentcited by 167

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 8; 2026: 6.

18 papers.

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  1. 2024Building Machines that Learn and Think with Peoplecited by 73
  2. 2024Clio: Privacy-Preserving Insights into Real-World AI Usecited by 72
  3. 2025Prototypical Human-AI Collaboration Behaviors from LLM-Assisted Writing in the Wildcited by 25
  4. 2025Working with AI: Measuring the Applicability of Generative AI to Occupationscited by 19
  5. 2024TRIP-PAL: Travel Planning with Guarantees by Combining Large Language Models and Automated Plannerscited by 17
  6. 2025News Source Citing Patterns in AI Search Systemscited by 10
  7. 2025ELI-Why: Evaluating the Pedagogical Utility of Language Model Explanationscited by 4
  8. 2024The Emerging Generative Artificial Intelligence Divide in the United Statescited by 2
  9. 2025Can Language Models Critique Themselves? Investigating Self-Feedback for Retrieval Augmented Generation at BioASQ 2025cited by 2
  10. 2026Aligning Large Language Models with Searcher Preferencescited by 2
  11. 2026AI Fiction in the Wildcited by 2
  12. 2025Speaking the Right Language: The Impact of Expertise Alignment in User-AI Interactionscited by 1
6 more
  1. 2026Adopt neq Adapt: Longitudinal Analyses of LLM Conversations in the Wildcited by 1
  2. 2026Learning from 53.6K Real-World Developer Edits of AI-Generated Codecited by 1
  3. 2025How good are LLMs at Retrieving Documents in a Specific Domain?cited by 0
  4. 2025Finding Diamonds in Conversation Haystacks: A Benchmark for Conversational Data Retrievalcited by 0
  5. 2026Q&A or Document-Based? The Effects of Interface Type on How Screen Reader Users Access Interconnected Documentscited by 0
  6. 2026BoundaryMORPH: Budgeted Reranking via Active Set Selection for Diffuse Retrievalcited by 0