What is the role of accuracy vs. relevance in evaluating Vision-Language Models?

When evaluating vision-language models (VLMs), accuracy and relevance serve distinct but interconnected roles. Accuracy measures how closely a model’s output aligns with ground-truth data or objective facts. For example, if a VLM describes an image of a dog as “a cat,” this is an accuracy failure. Metrics like precision, recall, or task-specific benchmarks (e.g., image captioning BLEU scores) often quantify this. Relevance, however, assesses whether the output meaningfully addresses the user’s query or context, even if it’s not strictly factual. For instance, if a user asks for a creative story about an image of a park, a relevant response might include plausible details about children playing, even if those details aren’t explicitly visible. While accuracy prioritizes correctness, relevance focuses on contextual alignment and usefulness.

The balance between accuracy and relevance depends on the application. In high-stakes domains like medical imaging or autonomous systems, accuracy is critical. A VLM analyzing X-rays must correctly identify anomalies, as errors could lead to misdiagnosis. Here, strict accuracy metrics (e.g., F1 scores) dominate evaluations. Conversely, in creative or assistive tools, relevance often takes precedence. For example, a design app using a VLM to generate logo ideas from sketches should prioritize diverse, contextually fitting concepts over pixel-perfect accuracy. Similarly, chatbots handling open-ended queries might need to infer user intent and provide relevant suggestions, even if some details are approximate. Developers must decide which aspect to prioritize based on user needs and potential risks.

Evaluating both metrics requires tailored approaches. Accuracy is typically measured using labeled datasets (e.g., COCO for object detection) or automated metrics like CLIPScore for text-image alignment. However, relevance is more subjective, often requiring human evaluation or specialized benchmarks like OK-VQA, which tests contextual reasoning. A practical challenge is handling scenarios where accuracy and relevance conflict. For example, a VLM might generate a caption stating, “A man feeds ducks in a pond,” when the image only shows a man near water. While the caption is relevant, it’s inaccurate. To address this, developers can use hybrid evaluation frameworks, combining automated accuracy checks with user feedback loops to assess relevance. Striking the right balance ensures models meet both technical and practical requirements.