  Multimodal Large Language Models (MLLMs) have recently demonstrated
impressive capabilities in multimodal understanding, reasoning, and
interaction. However, existing MLLMs prevalently suffer from serious
hallucination problems, generating text that is not factually grounded in
associated images. The problem makes existing MLLMs untrustworthy and thus
impractical in real-world (especially high-stakes) applications. To address the
challenge, we present RLHF-V, which enhances MLLM trustworthiness via behavior
alignment from fine-grained correctional human feedback. Specifically, RLHF-V
collects human preference in the form of segment-level corrections on
hallucinations, and performs dense direct preference optimization over the
human feedback. Comprehensive experiments on five benchmarks in both automatic
and human evaluation show that, RLHF-V can enable substantially more
trustworthy MLLM behaviors with promising data and computation efficiency.
Remarkably, using 1.4k annotated data samples, RLHF-V significantly reduces the
hallucination rate of the base MLLM by 34.8%, outperforming the concurrent
LLaVA-RLHF trained on 10k annotated data. The final model achieves
state-of-the-art performance in trustworthiness among open-source MLLMs, and
shows better robustness than GPT-4V in preventing hallucinations aroused from
over-generalization. We open-source our code, model, and data at
https://github.com/RLHF-V/RLHF-V.
