  Large Multimodal Models (LMM) are built across modalities and the
misalignment between two modalities can result in "hallucination", generating
textual outputs that are not grounded by the multimodal information in context.
To address the multimodal misalignment issue, we adapt the Reinforcement
Learning from Human Feedback (RLHF) from the text domain to the task of
vision-language alignment, where human annotators are asked to compare two
responses and pinpoint the more hallucinated one, and the vision-language model
is trained to maximize the simulated human rewards. We propose a new alignment
algorithm called Factually Augmented RLHF that augments the reward model with
additional factual information such as image captions and ground-truth
multi-choice options, which alleviates the reward hacking phenomenon in RLHF
and further improves the performance. We also enhance the GPT-4-generated
training data (for vision instruction tuning) with previously available
human-written image-text pairs to improve the general capabilities of our
model. To evaluate the proposed approach in real-world scenarios, we develop a
new evaluation benchmark MMHAL-BENCH with a special focus on penalizing
hallucinations. As the first LMM trained with RLHF, our approach achieves
remarkable improvement on the LLaVA-Bench dataset with the 94% performance
level of the text-only GPT-4 (while previous best methods can only achieve the
87% level), and an improvement by 60% on MMHAL-BENCH over other baselines. We
opensource our code, model, data at https://llava-rlhf.github.io.

Computer Vision and Pattern Recognition;Computation and Language