The text discusses the challenges in building Large Multimodal Models (LMMs) due to the disparity between multimodal data and text-only datasets. The researchers present LLaVA-RLHF, a vision-language model trained for enhanced multimodal alignment. They adapt the Reinforcement Learning from Human Feedback (RLHF) paradigm to fine-tune LMMs and address the problem of hallucinatory outputs. Their strategy improves multimodal alignment at a relatively low annotation cost and sets new performance records for LMMs. The code, model, and data are available to the public.
Overcoming Hallucinations in AI: How Factually Augmented RLHF Optimizes Vision-Language Alignment in Large Multimodal Models
Large Multimodal Models (LMMs), which combine visual and language modalities, have the potential to be powerful tools in the field of artificial intelligence. However, a significant obstacle in building LMMs is the lack of high-quality training data that aligns the two modalities effectively.
To address this challenge, researchers from several institutions have introduced a vision-language model called LLaVA-RLHF. This model leverages Reinforcement Learning from Human Feedback (RLHF), a universal and scalable alignment paradigm, to enhance multimodal alignment. The researchers collect human preferences to fine-tune LMMs and focus on recognizing hallucinations, or inaccurately generated outputs. This strategy improves alignment at a relatively low cost, making it a practical choice for training LMMs.
The researchers also propose the use of a superior visual encoder and a larger language model to further enhance the functionality of the reward model used in RLHF. Additionally, they introduce the Factually Augmented RLHF algorithm, which calibrates reward signals by supplementing them with extra information such as picture descriptions or ground-truth options. They also augment synthetic vision instruction tuning data with high-quality human-annotated multimodal data to improve the general capabilities of LMMs.
To evaluate the performance of LMMs in real-world scenarios, the researchers introduce a benchmark dataset called MMHAL-BENCH, which focuses on penalizing hallucinations. The LLaVA-RLHF model performs exceptionally well in their experimental assessment, setting new performance records in multiple evaluation metrics.
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In summary, the Factually Augmented RLHF approach and the LLaVA-RLHF model provide practical solutions for overcoming hallucinations and improving vision-language alignment in Large Multimodal Models.
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