多模态 / NAVIN RESEARCH
Evaluate Multimodal AI With Evidence-Level Metrics
Measure perception, grounding, extraction, reasoning, and abstention separately.
Measure perception, grounding, extraction, reasoning, and abstention separately.
简要回答: A single similarity score hides where a multimodal system fails. Evaluate whether it perceived the right region or time, extracted the right value, connected evidence correctly, and abstained when evidence was missing. Keep task slices for quality, language, and input corruption.
实施步骤
- Label source regions, timestamps, or pages where feasible.
- Define field-level or event-level correctness.
- Add impossible and insufficient-evidence cases.
- Review disagreements with the original media visible.
验证清单
- Track performance by modality and corruption type.
- Measure unsupported-claim rate.
- Test after preprocessing or compression changes.
常见错误
- Using text-only graders without media access.
- Ignoring abstention quality.
- Evaluating clean benchmark images only.
实际决策
先交付能够通过上述检查的最小设计。记录测试中使用的模型、提示词、工具、数据集与运行时版本，让后续变化可以被比较而不是被猜测。依赖供应商的限制、模型名称、价格或预览功能之前，请重新检查链接文档；这些细节变化往往快于本文的工程原则。
