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arxiv: 2404.04268 · v1 · pith:J5FHYUM2new · submitted 2024-03-19 · 💻 cs.IR · cs.AI· cs.CY· cs.SI

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

classification 💻 cs.IR cs.AIcs.CYcs.SI
keywords searchbingenginegenerativeenginespeopletaskscapabilities
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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 2 Pith papers

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

  1. Adopt $\neq$ Adapt: Longitudinal Analyses of LLM Conversations in the Wild

    cs.AI 2026-05 unverdicted novelty 6.0

    Longitudinal analysis of Bing Copilot users shows sticky individual LLM habits, activity-level differences in task complexity and success, and that WildChat is skewed toward power users.

  2. From Searchable to Non-Searchable: Generative AI and Information Diversity in Online Information Seeking

    cs.HC 2026-04 unverdicted novelty 6.0

    ChatGPT expands the diversity of user questions (80% non-searchable) but delivers less diverse responses than Google for comparable queries, creating a feedback loop that may constrain information exposure.