Training data (for vision instruction tuning) with previously available Multi-choice options, which alleviates the reward hacking phenomenon in RLHFĪnd further improves the performance. We propose a new alignmentĪlgorithm called Factually Augmented RLHF that augments the reward model withĪdditional factual information such as image captions and ground-truth Is trained to maximize the simulated human rewards. Responses and pinpoint the more hallucinated one, and the vision-language model Vision-language alignment, where human annotators are asked to compare two Learning from Human Feedback (RLHF) from the text domain to the task of To address the multimodal misalignment issue, we adapt the Reinforcement Textual outputs that are not grounded by the multimodal information in context. Misalignment between two modalities can result in 'hallucination', generating Large Multimodal Models (LMM) are built across modalities and the
