Despite the widespread adoption of Vision-Language Understanding (VLU)
benchmarks such as VQA v2, OKVQA, A-OKVQA, GQA, VCR, SWAG, and VisualCOMET, our
analysis reveals a pervasive issue affecting their integrity: these benchmarks
contain samples where answers rely on assumptions unsupported by the provided
context. Training models on such data foster biased learning and hallucinations
as models tend to make similar unwarranted assumptions. To address this issue,
we collect contextual data for each sample whenever available and train a
context selection module to facilitate evidence-based model predictions. Strong
improvements across multiple benchmarks demonstrate the effectiveness of our
approach. Further, we develop a general-purpose Context-AwaRe Abstention (CARA)
detector to identify samples lacking sufficient context and enhance model
accuracy by abstaining from responding if the required context is absent. CARA
exhibits generalization to new benchmarks it wasn't trained on, underscoring
its utility for future VLU benchmarks in detecting or cleaning samples with
inadequate context. Finally, we curate a Context Ambiguity and Sufficiency
Evaluation (CASE) set to benchmark the performance of insufficient context
detectors. Overall, our work represents a significant advancement in ensuring
that vision-language models generate trustworthy and evidence-based outputs in
complex real-world scenarios.

通过收集上下文数据并训练一个上下文选择模块来解决 VLU 基准测试中普遍存在的问题，防止模型依赖无法支持的假设进行训练，同时开发了一个通用的 CARA 检测器来增强模型准确性，并且通过创建 CASE 集评估不充分的上下文检测器的性能，这些工作显著推进了在复杂现实场景中生成可信可靠的视觉语言模型的发展。