| --- |
| language: |
| - en |
| license: cc-by-4.0 |
| task_categories: |
| - text-generation |
| --- |
| |
| ## Dataset Information |
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| 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](https://doi.org/10.48550/arXiv.2604.10968). |
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| ## Code Repository |
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| GitHub: https://github.com/infosenselab/yield |
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| ## Citing YIELD |
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| If you use this resource in your projects, please cite the following paper. |
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|
| ```bibtex |
| @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} |
| } |
| ``` |
