Large Language Models vs. Search Engines: Evaluating User Preferences Across Varied Information Retrieval Scenarios
This study embarked on a comprehensive exploration of user preferences between Search Engines and Large Language Models (LLMs) in the context of various information retrieval scenarios. Conducted with a sample size of 100 internet users (N=100) from across the United States, the research delved into...
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| Main Author | |
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| Format | Journal Article |
| Language | English |
| Published |
11.01.2024
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| Subjects | |
| Online Access | Get full text |
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| Summary: | This study embarked on a comprehensive exploration of user preferences
between Search Engines and Large Language Models (LLMs) in the context of
various information retrieval scenarios. Conducted with a sample size of 100
internet users (N=100) from across the United States, the research delved into
20 distinct use cases ranging from factual searches, such as looking up
COVID-19 guidelines, to more subjective tasks, like seeking interpretations of
complex concepts in layman's terms. Participants were asked to state their
preference between using a traditional search engine or an LLM for each
scenario. This approach allowed for a nuanced understanding of how users
perceive and utilize these two predominant digital tools in differing contexts.
The use cases were carefully selected to cover a broad spectrum of typical
online queries, thus ensuring a comprehensive analysis of user preferences. The
findings reveal intriguing patterns in user choices, highlighting a clear
tendency for participants to favor search engines for direct, fact-based
queries, while LLMs were more often preferred for tasks requiring nuanced
understanding and language processing. These results offer valuable insights
into the current state of digital information retrieval and pave the way for
future innovations in this field. This study not only sheds light on the
specific contexts in which each tool is favored but also hints at the potential
for developing hybrid models that leverage the strengths of both search engines
and LLMs. The insights gained from this research are pivotal for developers,
researchers, and policymakers in understanding the evolving landscape of
digital information retrieval and user interaction with these technologies. |
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| DOI: | 10.48550/arxiv.2401.05761 |