---
license: apache-2.0
datasets:
- helehan/topic-overwrite
language:
- en
---

# Model Card for Model ID

[GitHub](https://github.com/topic-overwrite/topic-level-overwrite/tree/main) | [Paper](https://arxiv.org/abs/2411.17265)

## Model Details

The model, trained using the RLHF/RLAIF methods proposed in the [TPO paper](https://arxiv.org/abs/2411.17265) by llava, has enhanced trustworthiness and reduced hallucinations.

## Model Description

- **Trained from model:** [llava-v1.5-7B](https://huggingface.co/liuhaotian/llava-v1.5-7b)
- **Trained on data:** [TPO-Dataset](https://huggingface.co/datasets/helehan/topic-overwrite)

## Usage

Please look at [GitHub](https://github.com/topic-overwrite/topic-level-overwrite/tree/main) for more details about usage.

## Citation

```bibtex
@article{he2024topic,
  title={A Topic-level Self-Correctional Approach to Mitigate Hallucinations in MLLMs},
  author={He, Lehan and Chen, Zeren and Shi, Zhelun and Yu, Tianyu and Shao, Jing and Sheng, Lu},
  journal={arXiv preprint arXiv:2411.17265},
  year={2024}
}
```