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

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arxiv 2404.04268 v1 pith:J5FHYUM2 submitted 2024-03-19 cs.IR cs.AIcs.CYcs.SI

classification cs.IRcs.AIcs.CYcs.SI
keywords searchbingenginegenerativeenginespeopletaskscapabilities
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. News Source Citing Patterns in AI Search Systems

    cs.IR 2025-07 conditional novelty 6.0 of 10

    AI search engines concentrate news citations among a few mostly left-leaning, high-quality outlets, and users do not appear to base preferences on cited source leaning or quality.

  2. ELI-Why: Evaluating the Pedagogical Utility of Language Model Explanations

    cs.CL 2025-06 conditional novelty 6.0 of 10

    ELI-Why shows GPT-4's grade-tailored explanations often miss the intended educational level and are less informative than human-curated explanations.

  3. Explainable Information Retrieval in the Audit Domain

    cs.IR 2025-07 conditional novelty 3.0 of 10

    A position paper proposing research directions and challenges for explainable information retrieval (XIR) in the audit domain, with no empirical results.

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