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Understand the language model architectures that serve as the reasoning engine in VLMs. From decoder-only transformers to encoder-decoder models, tokenization strategies for multimodal inputs, attention mechanisms over mixed sequences, popular LLM backbones, and the scaling laws that guide model selection.
The language model is the reasoning engine of every Vision-Language Model. While the vision encoder perceives the world and the bridge module translates visual features, it is the LLM backbone that performs inference, generates text, follows instructions, and chains together multi-step reasoning over what the model "sees."
Modern VLMs inherit their language capabilities from pretrained LLMs---GPT-class decoder-only transformers, encoder-decoder models like Flan-T5, or mixture-of-experts architectures like Mixtral. The choice of backbone profoundly affects the VLM's ability to follow complex instructions, perform multi-turn dialogue, reason about spatial relationships, and generate accurate descriptions.
A critical design question is how to extend a text-only LLM to process visual tokens. The model must handle a mixed sequence of image tokens and text tokens under a unified attention mechanism. This requires careful tokenization, positional encoding, and attention masking strategies.
This chapter covers:
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Autoregressive generation with causal masking — the dominant architecture behind GPT, LLaMA, and most modern VLMs.
Bidirectional encoding with cross-attention decoding — the T5/Flan architecture used in some VLMs like BLIP-2.
How vision features become tokens the language model can process — visual tokens, delimiters, and multi-image sequences.
Full causal self-attention vs gated cross-attention — how VLMs let language attend to visual information.
Which model and how large
LLaMA, Mistral, Phi, Qwen — the specific models used as VLM language backbones and their tradeoffs.
Chinchilla scaling, VLM-specific compute allocation, and quantization — choosing the right model size.
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