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Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
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Large vision-language models (LVLMs) have achieved impressive results in visual question-answering and reasoning tasks through vision instruction tuning on specific datasets. However, there remains significant room for improvement in aligning visual and language modalities. Existing methods often depend on external models or data, leading to uncontrollable and unstable alignment results. In this paper, we propose SIMA, a self-improvement framework that enhances visual and language modality alignment without external dependencies. SIMA leverages existing vision instruction tuning datasets to self-generate responses, incorporating an in-context self-critic mechanism that constructs preference pairs for tuning. Crucially, our approach allows LVLMs to act as critics by designing effective critic prompts, eliminating the need for additional fine-tuning with external instruction data. We introduce three novel visual metrics within the self-critic process to guide judgment, significantly improving the accuracy of self-critic. Through extensive experiments across 14 hallucination and comprehensive benchmarks, we demonstrate that SIMA significantly improves LVLM's performance and outperforms previous approaches, achieving superior modality alignment.
Forward citations
Cited by 6 Pith papers
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From Answers to Rationales: Self-Aligning Multimodal Reasoning with Answer-Oriented Chain-of-Thought
Answer-oriented chain-of-thought prompts that generate both positive and negative reasoning data, combined with iterative DPO, improve multimodal LLM reasoning on several benchmarks.
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LeanPO: Lean Preference Optimization for Likelihood Alignment in Video-LLMs
LeanPO improves Video-LLM alignment by using a reference-free average-likelihood reward, self-generated winning/losing pairs, and dynamic label smoothing, yielding gains on six video benchmarks.
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ASPO: Adaptive Sentence-Level Preference Optimization for Fine-Grained Multimodal Reasoning
ASPO's adaptive sentence-level loss, by the paper's own definitions, reduces exactly to the standard DPO loss, leaving no difference in the optimization objective.
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