metadata
language:
- en
license: cc-by-4.0
task_categories:
- text-generation
Dataset Information
Most conversational agents (CAs) are designed to satisfy user needs through user-driven interactions. However, many real-world settings, such as academic interviewing, judicial proceedings, and journalistic investigations, involve broader institutional decision-making processes and require agents that can elicit information from users. To enable systematic research on this setting, we present YIELD, a 26M-token dataset of 2,281 ethically sourced, human-to-human dialogues. For full details, see the accompanying paper here.
Code Repository
GitHub: https://github.com/infosenselab/yield
Citing YIELD
If you use this resource in your projects, please cite the following paper.
@misc{De_Lima_YIELD_A_Large-Scale_2026,
author = {De Lima, Victor and Yang, Grace Hui},
doi = {10.48550/arXiv.2604.10968},
title = {{YIELD: A Large-Scale Dataset and Evaluation Framework for Information Elicitation Agents}},
url = {https://arxiv.org/abs/2604.10968},
year = {2026}
}