Foundation Models Explained: Why Transformers Handle Language and Diffusion Models Paint Pictures

A foundation model is a large neural network trained on vast, broad datasets so that it can serve as a versatile base for many different tasks rather than a single purpose. Two architectural patterns dominate the field: Transformers, which process sequences of text and power language understanding and generation, and Diffusion models, which learn to reverse a noising process and dominate image generation. The architecture largely determines what the model can do well — and each carries documented risks, from bias to hallucination, that governance mechanisms such as AI Bills of Materials are being designed to address.

What are Foundation Models?

The name itself is a statement of intent. As researchers at the Alan Turing Institute put it, "the term Foundation Model reflects the expectation that these models can be the foundation for many different kinds of tasks and applications." Instead of building one model per problem — one for translation, another for summarization, a third for image editing — a foundation model is pre-trained once on a huge corpus and then adapted to many downstream uses.

IBM's explainer describes these models as large-scale systems whose defining trait is breadth: they are trained on massive datasets and then applied across a wide range of tasks. That breadth is what separates a foundation model from the narrower task-specific neural networks that preceded it. The same underlying model, with modest adaptation, can summarize a document, extract named entities from it, or draft new creative text.

It is worth being precise about what "foundation" does not mean. It does not mean the model understands the world the way a person does, nor that it is reliable by default. It means the model has absorbed enough statistical structure from its training data that many capabilities — some anticipated by its designers, some not — emerge from it. That combination of versatility and unpredictability is the thread running through everything that follows.

The Transformer Architecture: Powering Language Understanding

The Transformer, first introduced in the 2017 paper "Attention is All You Need," is the architecture that, in the words of the Georgia Tech–affiliated Transformer Explainer project, "fundamentally changed the approach to Artificial Intelligence." It has become the go-to architecture for deep learning models, powering text-generative systems like OpenAI's GPT, Meta's Llama, and Google's Gemini.

Its key innovation is self-attention. IBM's description is usefully concrete: a self-attention mechanism allows Transformers to "focus their attention" on the most important tokens in the input sequence, regardless of their position. In a long sentence, the model can weigh which earlier words matter for interpreting a later one, without processing the text strictly in order. This is what lets a Transformer hold long-range context — the reason a pronoun at the end of a paragraph can be tied to a name at the beginning.

A standard Transformer pairs two components: an encoder, which converts input into contextual representations called embeddings, and a decoder, which uses self-attention together with those embeddings to generate, in IBM's phrasing, "the most statistically probable output sequence." The decoder is, in effect, a very sophisticated probability engine: given everything so far, it predicts what plausibly comes next.

That mechanism explains both the strengths and the characteristic failure of language models. Because output is probabilistic, the model can produce fluent, coherent, creative text — and it can just as fluently produce statements that are false. The architecture has no built-in fact-checker; it has a next-token predictor.

Notably, the Transformer's reach extends beyond language. The Transformer Explainer notes that it is also applied in audio generation, image recognition, protein structure prediction, and even game playing — evidence of its versatility across numerous domains. But its stronghold, and the reason it anchors most foundation models, remains text.

Foundation Model Architectures, Modalities, and Capabilities

The clearest way to see why different foundation models excel at different tasks is to line the two architectures up side by side. The table below classifies each by its underlying design, the data modality it primarily handles, and the capabilities that follow from that pairing — the reasoning path being that architecture determines modality, and modality determines what the model is practically used for.

The comparison yields a simple mental model: Transformers are suited to language tasks because their self-attention mechanism is built for sequences, while Diffusion models are suited to image generation because their noise-and-reverse process naturally produces visual output. The two are distinct in their primary application, even though both sit under the foundation model umbrella.

Diffusion Models: Generating Creative Visuals

Diffusion models work almost like a sculptor in reverse. According to IBM, diffusion-based neural networks gradually "diffuse" training data with random noise, then learn to reverse that diffusion process to reconstruct the original data. Once trained, the model can start from pure noise and denoise its way to a new image that resembles what it saw in training — guided, in text-to-image systems, by a text prompt.

This is the architecture behind the best-known image generators. IBM identifies diffusion models as the primary architecture in text-to-image foundation models including Google's Imagen, OpenAI's DALL-E (starting with DALL-E 2), and Stability AI's Stable Diffusion. Google, for its part, describes Imagen as a specialized model for text-to-image generation, distinct from its general-purpose Gemini family — a reminder that within a single company's portfolio, different architectures are chosen for different jobs.

The capability that results is creative content generation in the visual domain: a user describes a scene in words, and the model produces a corresponding image. The same probabilistic caveat that applies to Transformers applies here. A diffusion model generates what is statistically plausible given its training data and prompt, not what is accurate or original in any human sense — and it inherits whatever biases and patterns its training images contained.

Key Capabilities of Foundation Models

What can these models actually do? Apple's developer documentation for its on-device foundation models gives a concrete task list: "a diverse range of text generation tasks, like summarization, entity extraction, text and image understanding, refinement, dialog for games, generating creative content, and more." That list is a useful reality check — most real-world use is practical text work, not spectacle.

The more scientifically interesting capabilities are the ones researchers call emergent — abilities that arise from scale rather than explicit programming. The Alan Turing Institute's research program on foundation model capabilities highlights common-sense reasoning: large language models such as GPT "appear to have some common-sense reasoning capabilities," but the researchers are careful to add that "the boundaries of these capabilities remains unclear." Their project aims to develop robust methods to evaluate how well such models understand the physical, causal, spatial, temporal, and social rules that govern the world.

The same program flags theory of mind — reasoning about the beliefs and aspirations of other people — as an open question: to what extent can this capability be acquired by training on textual datasets? It should be treated as an active research area, not an established feature. The honest summary is that these models demonstrably perform summarization, extraction, and creative generation today, while their deeper reasoning abilities are still being measured.

Those capabilities come with well-documented risks. Encyclopedic and industry overviews of foundation models consistently list hallucinations — the confident generation of false information, a direct consequence of probabilistic output — alongside algorithmic bias inherited from training data, and privacy and copyright concerns arising from the sheer volume of data consumed. None of these are edge cases; they are structural features of how the models work.

Governance responses are forming around transparency. Academic work on integrating foundation models into software systems has proposed AI Bills of Materials (AIBOMs) — machine-readable inventories of a model's components, data, and dependencies, analogous to software bills of materials — alongside standardized reporting frameworks so that downstream users can know what they are actually deploying. These mechanisms are proposals in development, not yet universal practice.

Navigating the Future of Foundation Models

Readers should approach foundation models with an understanding of their architectural strengths and inherent limitations, recognizing the need for ongoing governance and ethical considerations. The practical takeaway is architectural: if the task is language — summarizing, extracting, drafting — a Transformer-based model is the fit; if it is generating images from descriptions, a Diffusion model is. And in both cases, the model's output is probabilistic, which is precisely why hallucination and bias travel with the capability.

The measurable indicator to watch is the continued development of AI Bills of Materials (AIBOM) and standardized reporting frameworks for foundation models. As those governance tools mature from proposal to practice, organizations deploying foundation models will gain a concrete way to verify what a model contains and where its data came from — the missing piece that capability alone cannot supply.

Sources

• What Are Foundation Models? — IBM
• Transformer Explainer: LLM Transformer Model Visually Explained — Poloclub
• TURING
• Transformer (deep learning) - Wikipedia — EN
• What are foundation models? — Google Cloud
• Foundation Models — Apple Developer Documentation