Multimodal / 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.
Short answer: 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.
Implementation steps
- 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.
Validation checklist
- Track performance by modality and corruption type.
- Measure unsupported-claim rate.
- Test after preprocessing or compression changes.
Common mistakes
- Using text-only graders without media access.
- Ignoring abstention quality.
- Evaluating clean benchmark images only.
Practical decision
Ship the smallest design that passes the checks above. Record the model, prompt, tool, dataset, and runtime versions used in testing so later changes can be compared rather than guessed. Recheck the linked documentation before relying on provider-specific limits, model names, prices, or preview features; those details change more quickly than the engineering principles in this note.
