---
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}
}
```