  Large Vision-Language Models (LVLMs) have made substantial progress by
integrating pre-trained large language models (LLMs) and vision models through
instruction tuning. Despite these advancements, LVLMs often exhibit the
hallucination phenomenon, where generated text responses appear linguistically
plausible but contradict the input image, indicating a misalignment between
image and text pairs. This misalignment arises because the model tends to
prioritize textual information over visual input, even when both the language
model and visual representations are of high quality. Existing methods leverage
additional models or human annotations to curate preference data and enhance
modality alignment through preference optimization. These approaches may not
effectively reflect the target LVLM's preferences, making the curated
preferences easily distinguishable. Our work addresses these challenges by
proposing the Calibrated Self-Rewarding (CSR) approach, which enables the model
to self-improve by iteratively generating candidate responses, evaluating the
reward for each response, and curating preference data for fine-tuning. In the
reward modeling, we employ a step-wise strategy and incorporate visual
constraints into the self-rewarding process to place greater emphasis on visual
input. Empirical results demonstrate that CSR enhances performance and reduces
hallucinations across ten benchmarks and tasks, achieving substantial
improvements over existing methods by 7.62%. Our empirical results are further
supported by rigorous theoretical analysis, under mild assumptions, verifying
the effectiveness of introducing visual constraints into the self-rewarding
paradigm. Additionally, CSR shows compatibility with different vision-language
models and the ability to incrementally improve performance through iterative
fine-tuning. Our data and code are available at
https://github.com/YiyangZhou/CSR.
