The Use of Generative Search Engines for Knowledge Work and Complex Tasks
This addresses how generative search engines impact information retrieval for users, showing a shift towards more complex tasks, but it is incremental as it builds on existing LLM and search technologies.
The study analyzed Bing Copilot, a generative search engine combining LLMs with traditional search, finding that users engage in more knowledge work tasks with higher cognitive complexity compared to traditional search engines.
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.