Multimodal LLMs: Vision, Audio, and Document Understanding (ANSWERED)
Model question on multimodal LLMs — image/audio tokenization, document QA, OCR pipelines, and latency/cost trade-offs.
TL;DR — Quick Answer
Multimodal LLMs encode non-text inputs (images, audio, PDF pages) into token-like representations fused with text in the Transformer. Vision: patch encoders (ViT) project image regions to embeddings the LLM attends to. Audio: spectrogram or speech encoder tokens. Documents: OCR + layout analysis or native PDF/image input. Production challenges: higher latency/cost per request, hallucinated visual details, PII in images, large payloads, and need for pre-processing (resize, redact) plus grounding for charts and tables.
The Interview Question
Explain multimodal LLMs that handle vision, audio, and documents. How do they work at a high level and what production challenges arise?
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