An iterative self-training method using DPO on self-generated score-conditioned explanations improves both image scoring accuracy and score-explanation consistency in VLMs.
Main Results Table 1 presents the results of our method applied to the LLaV A-NeXT-7B model on the A V A dataset, a part of which is also plotted in Fig
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Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation
An iterative self-training method using DPO on self-generated score-conditioned explanations improves both image scoring accuracy and score-explanation consistency in VLMs.