Multimodal · Training Data, Evaluation, and Serving

This module covers collecting and aligning multimodal training data, along with issues requiring dedicated validation before production: benchmark contamination, image- and generated-content-specific safety risks, and inference-serving architecture.

Chapters

1. Chapter 9: Multimodal Training Data and Alignment
2. Chapter 10: Multimodal Evaluation, Safety, and Inference Serving

Connections within the module

flowchart LR
    D[Multimodal Training Data<br/>and Alignment] --> E[Multimodal Evaluation,<br/>Safety, and Inference Serving]

Chapter 9 examines how data supplies or omits training signals; Chapter 10 asks how to verify capabilities, risks, and serving costs. These are not finishing touches added after training: splits, decontamination, permissions, and resource budgets must be determined during data collection and system design.

Important distinctions include: similarity is not pair correctness, a release date does not establish freedom from contamination, content credentials do not establish factual truth, and media-feature caching does not permit arbitrary-context KV reuse. For the general framework, see LLM · Multimodal Models.

Return to Multimodal Topics.