2025
VERITAS: Leveraging Vision Priors and Expert Fusion to Improve Multimodal Data
EMNLP 2025
The quality of supervised fine-tuning (SFT) data is crucial for the performance of large multimodal models (LMMs), yet current data enhancement methods often suffer from factual errors and hallucinations due to inadequate visual perception. To address this challenge, we propose VERITAS, a pipeline t