FiVL：从训练、评估与可解释性视角改进视觉-语言对齐的框架
FiVL: A Framework for Improved Vision-Language Alignment through the Lens of Training, Evaluation and Explainability
AI总结：
针对大视觉语言模型视觉 grounding 不足、现有基准难衡量答案对视觉依赖度的问题，提出FiVL数据集构建方法，通过新训练任务、专用基准及注意力头识别，提升模型视觉对齐能力并提供可解释性。
摘要：
大视觉语言模型（LVLMs）在整合视觉与文本输入以实现多模态推理方面已取得显著进展。但一个反复出现的挑战是，当需要两种模态共同得出准确答案时，确保这些模型能像利用语言内容一样有效利用视觉信息。我们假设，当前LVLMs产生幻觉的原因是缺乏有效的视觉 grounding（视觉 grounding）。此外，当前的视觉-语言基准并未专门衡量答案对视觉输入的依赖程度。这一局限导致难以确认图像是否真的必要，尤其是在视觉问答等任务中。本研究提出FiVL，这是一种构建数据集的新方法，既可用于训练LVLMs以增强其视觉 grounding 能力，也可用于评估模型在该方面的实际效果。我们通过三种方式验证了数据集的价值：第一，基于我们增强后的训练数据集提出了一种新的训练任务，其性能优于基线模型；第二，提出了相关基准，用于评估模型将图像作为实质性证据而非仅依赖语言先验的能力；第三，识别出视觉-语言对齐最强的注意力头，为视觉驱动的幻觉问题提供了可解释性。代码可在https://github.com/IntelLabs/fivl获取。
英文摘要：
Large Vision Language Models (LVLMs) have achieved significant progress in integrating visual and textual inputs for multimodal reasoning. However, a recurring challenge is ensuring these models utilize visual information as effectively as linguistic content when both modalities are necessary to formulate an accurate answer. We hypothesize that hallucinations arise due to the lack of effective visual grounding in current LVLMs. Furthermore, current vision-language benchmarks are not specifically measuring the degree to which the answer require the visual input. This limitation makes it challenging to confirm that the image is truly necessary, particularly in tasks like visual question answering. In this work, we introduce FiVL, a novel method for constructing datasets designed to train LVLMs for enhanced visual grounding and also evaluate their effectiveness in achieving it. We demonstrate the value of our datasets through three approaches. First, we introduce a novel training task based on our augmented training dataset, resulting in better performance than the baseline. Second, we present benchmarks to assess the model's ability to use image as substantive evidence, rather than relying solely on linguistic priors. Finally, we identify attention heads with the strongest vision-language alignment, enabling explainability on visual-driven hallucinations. The code is available at https://github.com/IntelLabs/fivl.
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