Identifying and Resolving Pitfalls of Knowledge-Based VQA Benchmarks: Auditing, Repairing, and Augmenting
arXiv:2607.00159v1 Announce Type: new Abstract: Knowledge-Based Visual Question Answering (KB-VQA) aims to evaluate whether Visual Language Models (VLMs) can retrieve, ground, and reason over external structured knowledge beyond visual evidence. In practice, answer accuracy is widely adopted as the primary evaluation metric, implicitly treating correctness as a proxy for knowledge-grounded reasoning. However, for existing KB-VQA benchmarks, this proxy relies on critical assumptions that are often overlooked and rendered unreliable by benchmark issues: annotated answer must be derivable from th
The proliferation of Visual Language Models (VLMs) and the increasing reliance on benchmarks for evaluation necessitate a critical look at the assumptions underlying these metrics.
Reliable knowledge-based VQA benchmarks are crucial for accurately assessing and improving the reasoning capabilities of advanced AI models, impacting investment and development trajectories.
The proposed work highlights flaws in current VQA evaluation, suggesting a need for more robust benchmarks that genuinely reflect knowledge-grounded reasoning, potentially re-aligning research priorities.
- · AI researchers focused on robust evaluation
- · Developers of foundational AI models
- · Users relying on VLM accuracy
- · Developers relying on flawed benchmarks
- · Companies with less sophisticated evaluation methods
Improved VQA benchmarks will lead to better understanding of VLM limitations and capabilities.
Enhanced evaluation methodologies could accelerate the development of more reliable and trustworthy AI systems.
The push for robust and auditable AI could influence future regulatory frameworks and commercial adoption strategies for VLMs.
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Read at arXiv cs.CL