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

arXiv: https://arxiv.org/abs/2404.04268 · HTML: https://mlsanity.com/paper/2404.04268/

## 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 (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023)
- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)

## Built on by (most cited first, 18 of 18)

- [Building Machines that Learn and Think with People](https://mlsanity.com/paper/2408.03943/) (2024)
- [Clio: Privacy-Preserving Insights into Real-World AI Use](https://mlsanity.com/paper/2412.13678/) (2024)
- [Prototypical Human-AI Collaboration Behaviors from LLM-Assisted Writing in the Wild](https://mlsanity.com/paper/2505.16023/) (2025)
- [Working with AI: Measuring the Applicability of Generative AI to Occupations](https://mlsanity.com/paper/2507.07935/) (2025)
- [TRIP-PAL: Travel Planning with Guarantees by Combining Large Language Models and Automated Planners](https://mlsanity.com/paper/2406.10196/) (2024)
- [News Source Citing Patterns in AI Search Systems](https://mlsanity.com/paper/2507.05301/) (2025)
- [ELI-Why: Evaluating the Pedagogical Utility of Language Model Explanations](https://mlsanity.com/paper/2506.14200/) (2025)
- [The Emerging Generative Artificial Intelligence Divide in the United States](https://mlsanity.com/paper/2404.11988/) (2024)
- [Can Language Models Critique Themselves? Investigating Self-Feedback for Retrieval Augmented Generation at BioASQ 2025](https://mlsanity.com/paper/2508.05366/) (2025)
- [Aligning Large Language Models with Searcher Preferences](https://mlsanity.com/paper/2603.10473/) (2026)
- [AI Fiction in the Wild](https://mlsanity.com/paper/2606.22748/) (2026)
- [Speaking the Right Language: The Impact of Expertise Alignment in User-AI Interactions](https://mlsanity.com/paper/2502.18685/) (2025)
- [Adopt neq Adapt: Longitudinal Analyses of LLM Conversations in the Wild](https://mlsanity.com/paper/2605.29018/) (2026)
- [Learning from 53.6K Real-World Developer Edits of AI-Generated Code](https://mlsanity.com/paper/2607.25130/) (2026)
- [How good are LLMs at Retrieving Documents in a Specific Domain?](https://mlsanity.com/paper/2509.22658/) (2025)
- [Finding Diamonds in Conversation Haystacks: A Benchmark for Conversational Data Retrieval](https://mlsanity.com/paper/2510.02938/) (2025)
- [Q&A or Document-Based? The Effects of Interface Type on How Screen Reader Users Access Interconnected Documents](https://mlsanity.com/paper/2608.25382/) (2026)
- [BoundaryMORPH: Budgeted Reranking via Active Set Selection for Diffuse Retrieval](https://mlsanity.com/paper/2609.27213/) (2026)

## 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.