Synth-Align: Improving Trustworthiness in Vision-Language Model with Synthetic Preference Data Alignment

The introduction of Synth-Align marks a significant advancement in the field of artificial intelligence, particularly in improving large vision-language models (LVLMs). These models have been noted for their potential but are often hindered by hallucinations that compromise their reliability. Synth-Align addresses this issue by generating synthetic human-preference image-text data, which aids in aligning model outputs with user expectations. The framework has demonstrated impressive results, achieving 87.6% accuracy and reducing hallucination rates by nearly 51%. This development not only enhances the performance of LVLMs but also contributes to a more robust user experience, making AI applications more trustworthy and effective in real-world scenarios.