arXiv:2605.25036v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) extend large language models with visual understanding, but remain vulnerable to hallucination, where outputs are fluent yet inconsistent with images. Recent studies link this issue to language bias-the tendency of LVLMs to over-rely on text while neglecting visual inputs. Yet most analyses remain empirical without uncovering its underlying cause. In this paper, we provide a systematic study of language bias and identify its root in modality misalignment during training. Our analysis shows that both Visual Ins
The rapid advancement and deployment of LVLMs make their inherent biases and limitations, particularly hallucination, a critical area of research at this moment.
Understanding and mitigating language bias in LVLMs is vital for their reliable application across various industries, ensuring outputs are consistent with visual inputs and not just fluent.
This research provides a foundational understanding of language bias's root cause (modality misalignment) and proposes effective mitigation strategies, enhancing the trustworthiness and utility of LVLMs.
- · AI developers and researchers
- · Industries deploying LVLMs (e.g., healthcare, automotive)
- · Users of AI-powered vision systems
- · Developers ignoring bias mitigation
- · Current LVLMs with significant hallucination rates
Improved reliability and accuracy of LVLM applications across various domains.
Increased user trust and broader adoption of advanced AI systems that combine vision and language.
Accelerated development of more robust, multimodal AI architectures less susceptible to human-like cognitive biases.
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