Unverified50% confidenceFactExact time
Multimodal large language models typically map image/speech features into the same embedding space as text via projection layers or adapters, which are then processed uniformly by the Transformer decoder.
1
Sources
50%
Confidence
Long-term
Relevance
9/24/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedTraditional multimodal models adopt a modular splicing approach, using visual encoders such as CLIP or ViT to convert images into feature vectors before inputting them into the language model, resulting in information bottlenecks between modules79% similarUnverifiedThe Transformer architecture underlying large language models does not directly process raw characters; instead, it first uses a tokenizer to split text into subword units, which are then mapped to high-dimensional vectors for computation.77% similarVerifiedLarge language models are based on the Transformer architecture and are trained on massive amounts of text data.76% similarUnverifiedLarge language models can understand multimodal inputs including text, images, video, and audio.75% similarUnverifiedMultimodal large language models align visual encoders (such as CLIP and ViT) with language models through contrastive learning.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/946094API
curl https://kongchang.com/api/v1/knowledge/claims/946094MCP
get_claim(id=946094)