PDS-DPO-7B-LoRA Model Card

GitHub | arXiv

PDS-DPO-7B is a vision-language model built upon LLaVA 1.5 7B and trained using the proposed Preference Data Synthetic Direct Preference Optimization (PDS-DPO) framework. This approach leverages synthetic data generated using generative and reward models as proxies for human preferences to improve alignment, reduce hallucinations, and enhance reasoning capabilities.

Model Details

• Model Name: PDS-DPO-7B-LoRA
• Base Model: LLaVA 1.5 (Vicuna-7B)
• Framework: Preference Data Synthetic Alignment using Direct Preference Optimization (PDS-DPO)
• Dataset: 9K synthetic image-text pairs (positive and negative responses), generated via Stable Diffusion, LLaVA, and scored by reward models like ImageReward and Llama-3-8B-ArmoRM.
• Training Hardware: 2 × A100 GPUs
• Training Optimization: LoRA fine-tuning

Key Features

• Synthetic Data Alignment
  • Utilizes generative models and leverages reward models for quality control, filtering the best images and responses to align with human preferences.
• Improved Hallucination Control
  • Achieves significant reduction in hallucination rates on benchmarks like Object HalBench, MMHal-Bench, and POPE.
• Competitive Benchmark Performance
  • Demonstrates strong results across vision-language tasks like VQAv2, SQA, MM-Vet, and TextVQA.

Examples

Citation

@article{wijaya2024multimodal,
  title={Multimodal Preference Data Synthetic Alignment with Reward Model},
  author={Wijaya, Robert and Nguyen, Ngoc-Bao and Cheung, Ngai-Man},
  journal={arXiv preprint arXiv:2412.17417},
  year={2024}
}