| base_model: | |
| - meta-llama/Llama-3.1-8B-Instruct | |
| datasets: | |
| - infosense/yield | |
| language: | |
| - en | |
| license: llama3.1 | |
| library_name: peft | |
| pipeline_tag: text-generation | |
| # YIELD Fine-Tuning Adapters | |
| This repository contains the persona adapter models presented in the paper [YIELD: A Large-Scale Dataset and Evaluation Framework for Information Elicitation Agents](https://huggingface.co/papers/2604.10968). | |
| ## Model Information | |
| All adapters in this repository are LoRA adapters trained on top of the `Llama-3.1-8B-Instruct`, `Llama-3.2-3B-Instruct`, and `DeepSeek-R1-Distill-Llama-8B` models. All models are fine-tuned using both the Supervised Fine-Tuning (SFT) and Offline Reinforcement-Learning (ORL) pipelines detailed in the paper. These models are designed to act as Information Elicitation Agents (IEAs), which aim to elicit information from users to support institutional or task-oriented objectives. | |
| ## Resources | |
| - **Code Repository**: [GitHub - infosenselab/yield](https://github.com/infosenselab/yield) | |
| - **Dataset**: [infosense/yield](https://huggingface.co/datasets/infosense/yield) | |
| - **Paper**: [Hugging Face Papers](https://huggingface.co/papers/2604.10968) | |
| ## Citing YIELD | |
| If you use this resource in your projects, please cite the following paper: | |
| ```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} | |
| } | |
| ``` |