User Interaction Dataset for CHI 2025 paper "Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistant."

DOI:10.4121/d34aa48b-9722-4ad4-b108-a62878c1feca.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/d34aa48b-9722-4ad4-b108-a62878c1feca
Datacite citation style:
He, Gaole; Demartini, Gianluca; Gadiraju, Ujwal (2025): User Interaction Dataset for CHI 2025 paper "Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistant.". Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/d34aa48b-9722-4ad4-b108-a62878c1feca.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Software

This repo contains all code, data, and user interfaces associated with paper "Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistant." In our study, we analyzed different extents of user involvement in the planning and execution stages of LLM agents. Our data is evaluated based on action sequences. We also recorded how users interact with LLM agents and provided an interface built upon Flask.

History

  • 2025-02-06 first online, published, posted

Publisher

4TU.ResearchData

Format

table/csv, text/txt

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Software Technology, Web Information Systems Group

DATA

To access the source code, use the following command:

git clone https://data.4tu.nl/v3/datasets/5ff47fde-d960-4caf-806f-214d9e491276.git "CHI2025_Plan-then-Execute_LLMAgent"

Or download the latest commit as a ZIP.

Files (4)