Editing Multimodal AI (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Expert multimodal evaluation requires specialized benchmarks for each task pair:

'''VQA benchmarks''': VQA v2, TextVQA (requires reading text in images), DocVQA (document understanding), ChartQA (chart/graph comprehension), MathVista (mathematical visual reasoning).

'''Image generation evaluation''': FID for overall quality; CLIP score for text-image alignment; human preference studies for aesthetics; T2I-CompBench for compositional accuracy (object counts, spatial relationships, attributes).

'''ASR evaluation''': Word Error Rate (WER) on LibriSpeech, Common Voice, and other diverse speech datasets. Evaluate separately on accented speech, noisy environments, and domain-specific vocabulary.

'''Holistic VLM benchmarks''': MMMU (Massive Multi-discipline Multimodal Understanding) — tests college-level questions across 30 subjects with images; MMBench, SEED-Bench.

Expert practitioners are especially attentive to '''calibration across difficulty levels''' — most VLM benchmarks have many easy questions that all models solve; the differentiation happens on hard cases. Report performance on hard subsets, not just overall accuracy.
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