Model
Full Form
Key Workflow (From Image)
Real-Life Example
LLM
Large Language Model
Input → Tokenization → Embedding → Transformer → Output
ChatGPT: Summarizes research papers, answers questions, or writes emails.
LCM
Language Comprehension Model
Input → Sentence Segmentation → SONAR Embedding → Diffusion → Advanced Patterning / Hidden Process → Quantization → Output
Google Search (BERT/PaLM): Accurately answers complex search queries like “Can I carry food through airport security?”
LAM
Language Action Model
Input Processing → Perception System → Intent Recognition → Task Breakdown / Memory System → Neuro-symbolic Integration → Action Planning / Quantization → Feedback Integration
Robot Assistants: Follows instructions like “Stack those boxes by height” by breaking it into sub-actions.
MoE
Mixture of Experts
Input → Router Mechanism → Experts → Top-K Selection → Weighted Combination → Output
Google’s Switch Transformer: Translates text using specialized expert models depending on context (e.g., language, domain).
VLM
Vision-Language Model
Image Input / Text Input → Vision Encoder / Text Encoder → Projection Interface → Multimodal Processor → Language Model → Output Generation
CLIP, Gemini: Helps blind users by describing the content of photos or signs they capture.
SLM
Small Language Model
Input Processing → Compact Tokenization → Efficient Transformer → Model Quantization / Memory Optimization → Edge Deployment → Output Generation
Siri (Offline Mode): Understands and processes commands like “set alarm” without internet.
MLM
Masked Language Model
Text Input → Token Masking → Embedding Layer → Left/Right Context → Bidirectional Attention → Masked Token Prediction → Feature Representation
Grammarly (via BERT): Suggests grammar corrections like changing “She go” to “She goes” using context.
SAM
Segment Anything Model
Prompt Input / Image Input → Prompt Encoder / Image Encoder → Image Embedding → Feature Correlation → Mask Decoder → Segmentation Output
Medical Imaging Tools (SAM): Segments tumors in MRI scans with a single prompt or click.
The Evolution of Language Models: From Comprehension to Action and Beyond
The field of natural language processing (NLP) has seen a remarkable journey, with language models evolving from simple statistical tools to sophisticated systems that understand, generate, and even act on human language. This blog traces the chronological development of key language model paradigms—Masked Language Models, Language Comprehension Models, Large Language Models, Small Language Models, Mixture of Experts, Vision-Language Models, Language Action Models, and the Segment Anything Model—explaining what they are, how each built on its predecessors, and the technical innovations that drove their evolution. Let’s dive into the story of how these models transformed AI, culminating in the cutting-edge advancements of 2025.
Chronological Order of Language Model Evolution
Based on their conceptual and technical emergence in NLP and AI research, the models are reordered as follows:
Masked Language Model (~2018)
Language Comprehension Model (~2018–2019)
Large Language Model (~2020)
Small Language Model (~2020–2021)
Mixture of Experts (~2021–2022)
Vision-Language Model (~2021–2022)
Language Action Model (~2023–2024)
Segment Anything Model (~2023, adapted to multimodal contexts by 2025)
Note: The timelines are approximate, based on when these paradigms became prominent in research or deployment. Terms like “Language Action Model” and “Segment Anything Model” are less standard in NLP, so the are interpreted contextually (e.g., Language Action Model as models integrating language with decision-making, and Segment Anything Model as a vision-focused model with NLP relevance in multimodal systems).
1. Masked Language Model (~2018)
What Is It?
Masked Language Models (MLMs) predict missing words in a sentence by leveraging surrounding context, a breakthrough for learning contextualized word representations. The most famous example is BERT (Bidirectional Encoder Representations from Transformers, Devlin et al., 2018), which uses the Transformer’s encoder to understand text bidirectionally.
How It Works
Architecture: Built on the Transformer’s encoder (Attention Is All You Need, Vaswani et al., 2017), MLMs use multi-head self-attention to process entire sequences, capturing relationships between words. BERT stacks 12 (Base) or 24 (Large) layers.
Training: Pre-trained on unlabeled text (e.g., Wikipedia, 3.3B words) with two objectives:
Masked Language Modeling: Randomly mask 15% of tokens (e.g., “The cat [MASK] on the mat” → predict “sat”).
Next Sentence Prediction (NSP): Predict if two sentences are consecutive, aiding tasks like question answering.
Output: Contextualized word embeddings, fine-tuned for tasks like classification or named entity recognition.
Key Paper: Devlin et al., 2018: “BERT obtains new state-of-the-art results on eleven natural language processing tasks.”
Role in Evolution
MLMs marked a shift from static word embeddings (e.g., Word2Vec, 2013) to contextual representations. Unlike unidirectional models, BERT’s bidirectional approach, inspired by the Transformer’s encoder, captured richer context, setting a new standard for language understanding.
2. Language Comprehension Model (~2018–2019)
What Is It?
Language Comprehension Models focus on deep understanding of text for tasks like question answering, sentiment analysis, or text classification. BERT is a prime example, but this category also includes models like RoBERTa (2019) and ALBERT (2019), which refined BERT’s approach.
How It Works
Architecture: Uses Transformer encoders, similar to MLMs, with stacked layers of self-attention and feedforward networks. RoBERTa, for instance, optimizes BERT by removing NSP and training on more data.
Training: Pre-trained on large corpora with tasks like MLM, then fine-tuned on labeled datasets. RoBERTa uses dynamic masking and larger datasets (160GB of text).
Output: Rich, task-specific representations (e.g., a [CLS] token vector for classification).
Key Paper: Liu et al., 2019 (RoBERTa): “A robustly optimized BERT pretraining approach, achieving better performance by training longer on more data.”
How It Evolved from MLMs
Technical Transition: Language Comprehension Models directly extend MLMs, with BERT as the pioneer. The Transformer’s encoder, introduced by Vaswani et al., provided the bidirectional self-attention mechanism that MLMs used for pre-training. Subsequent models like RoBERTa improved efficiency by tweaking pre-training (e.g., removing NSP, using larger batches) and increasing data scale, enhancing comprehension accuracy.
Why It Happened: BERT’s success (7% improvement on GLUE benchmarks) showed that pre-training on unlabeled data could generalize across tasks, prompting researchers to refine the approach for better performance and scalability.
3. Large Language Model (~2020)
What Is It?
Large Language Models (LLMs) are massive, general-purpose models capable of understanding and generating human-like text. Examples include GPT-3 (Brown et al., 2020) and T5 (Raffel et al., 2020), which scale up Transformer architectures to billions of parameters.
How It Works
Architecture: LLMs use the full Transformer (encoder-decoder, as in T5) or decoder-only architectures (as in GPT-3). GPT-3 has 175B parameters, with 96 layers of decoder-style self-attention.
Training: Pre-trained on vast datasets (e.g., GPT-3 on 570GB of text) using tasks like autoregressive language modeling (predicting the next token). Fine-tuning or few-shot learning adapts them to tasks like translation, summarization, or dialogue.
Output: Generates coherent text or provides embeddings for understanding tasks, often via prompting (e.g., “Write a story about…”).
Key Paper: Brown et al., 2020 (GPT-3): “Larger models exhibit strong zero-shot performance, reducing the need for fine-tuning.”
How It Evolved from Language Comprehension Models
Technical Transition: LLMs built on the Transformer’s scalability and BERT’s pre-training paradigm. While BERT focused on encoder-only comprehension, LLMs like GPT-3 used decoder-only architectures for generation, scaling up parameters (from BERT’s 340M to GPT-3’s 175B) and datasets. T5 combined encoder-decoder architectures, unifying comprehension and generation tasks under a “text-to-text” framework.
Why It Happened: BERT’s success showed that pre-training on large corpora could create versatile models. Researchers scaled this idea, leveraging advances in compute (e.g., TPUs) and data availability to build LLMs that generalize across tasks without task-specific fine-tuning, relying on in-context learning.
4. Small Language Model (~2020–2021)
What Is It?
Small Language Models (SLMs) are compact versions of LLMs, designed for efficiency on resource-constrained devices. Examples include DistilBERT (2019) and TinyBERT (2020), which distill larger models’ knowledge into smaller architectures.
How It Works
Architecture: Uses scaled-down Transformer encoders or decoders (e.g., DistilBERT has 6 layers, 66M parameters vs. BERT’s 110M). Retains self-attention and feedforward layers but reduces dimensionality.
Training: Trained via knowledge distillation, where a smaller model learns to mimic a larger model’s outputs, or directly pre-trained on smaller datasets. Fine-tuning follows BERT’s approach.
Output: Similar to LLMs but optimized for speed and lower memory use, suitable for edge devices.
Key Paper: Sanh et al., 2019 (DistilBERT): “A distilled version of BERT with 40% fewer parameters and 60% faster inference.”
How It Evolved from Large Language Models
Technical Transition: SLMs emerged as a response to LLMs’ computational demands. Distillation techniques transferred knowledge from large models (e.g., BERT, GPT) to smaller ones, reducing layers and parameters while preserving performance. For example, DistilBERT compresses BERT’s encoder by training on its soft targets (probability distributions).
Why It Happened: LLMs like GPT-3 required massive compute, making them impractical for mobile or edge applications. SLMs addressed this by leveraging the same Transformer architecture but optimizing for efficiency, driven by the need for real-world deployment.
5. Mixture of Experts (~2021–2022)
What Is It?
Mixture of Experts (MoE) models use multiple specialized sub-networks (“experts”) within a Transformer, activated selectively based on input. Examples include Switch Transformer (2021) and GLaM (2021), which scale to trillions of parameters efficiently.
How It Works
Architecture: Extends the Transformer with a gating mechanism that routes inputs to a subset of experts (smaller neural networks). Each expert handles specific patterns, reducing computation. For example, Switch Transformer uses 1.6T parameters but activates only a fraction per input.
Training: Pre-trained like LLMs, often with sparse activation to save compute. Fine-tuning adapts experts to specific tasks.
Output: Similar to LLMs, supporting generation or comprehension, but more efficient due to sparse computation.
Key Paper: Fedus et al., 2021 (Switch Transformer): “Sparse MoE models achieve high performance with sub-linear computational cost.”
How It Evolved from Small and Large Language Models
Technical Transition: MoE models built on LLMs’ scalability, using the Transformer’s modular structure. Instead of uniformly scaling parameters, MoE introduces sparsity by activating only relevant experts, inspired by the efficiency goals of SLMs. The gating mechanism (e.g., softmax over expert scores) was a novel addition to the Transformer’s attention and feedforward layers.
Why It Happened: LLMs’ computational costs spurred research into efficiency. SLMs showed that smaller models could perform well, but MoE took this further by combining the scale of LLMs with sparse activation, enabling trillion-parameter models without proportional compute costs.
6. Vision-Language Model (~2021–2022)
What Is It?
Vision-Language Models (VLMs) integrate text and visual processing, enabling tasks like image captioning or visual question answering. Examples include CLIP (2021) and DALL·E 2 (2022), which combine Transformer-based language models with vision models.
How It Works
Architecture: Uses dual Transformer streams: one for text (often a GPT-like decoder) and one for images (e.g., Vision Transformer, ViT). CLIP pairs a text encoder with an image encoder, trained to align representations in a shared space.
Training: Pre-trained on paired image-text data (e.g., 400M image-caption pairs for CLIP) using contrastive learning, where matching image-text pairs are pulled closer and non-matching pairs are pushed apart.
Output: Generates text (e.g., captions), answers questions about images, or produces images from text.
Key Paper: Radford et al., 2021 (CLIP): “Learning transferable visual models from natural language supervision.”
How It Evolved from Mixture of Experts and LLMs
Technical Transition: VLMs extended the Transformer’s versatility, inspired by LLMs’ ability to handle diverse tasks and MoE’s modular design. The Transformer’s attention mechanism was adapted for vision (e.g., ViT splits images into patches, treating them like tokens). CLIP’s dual-encoder approach built on BERT’s encoder for text and ViT for images, aligning them via contrastive loss rather than MLM.
Why It Happened: LLMs showed Transformers could handle large-scale pre-training, and MoE models proved modularity could scale efficiently. VLMs applied these ideas to multimodal data, driven by the need to integrate vision and language for real-world applications like image search or content generation.
7. Language Action Model (~2023–2024)
What Is It?
Language Action Models (LAMs) integrate language understanding with decision-making or action execution, often for robotics or interactive systems. While not a standard term, I interpret this as models like PaLM-E (2023) or those used in AI agents that combine language with planning or control (e.g., for robotic navigation).
How It Works
Architecture: Combines a language model (e.g., Transformer decoder like PaLM) with action-oriented components (e.g., policy networks for robotics). PaLM-E integrates a language encoder-decoder with vision and sensor inputs.
Training: Pre-trained on text and multimodal data, then fine-tuned on task-specific datasets (e.g., robot trajectories). Reinforcement learning or supervised learning aligns language instructions with actions.
Output: Generates actions (e.g., robot motor commands) or plans based on language inputs (e.g., “Move to the kitchen”).
Key Paper: Driess et al., 2023 (PaLM-E): “An embodied multimodal language model for robotic tasks.”
How It Evolved from Vision-Language Models
Technical Transition: LAMs built on VLMs’ multimodal capabilities, extending the Transformer to process not just text and images but also sensor data or action spaces. The Transformer’s attention mechanism was adapted to align language with action sequences, often using cross-modal attention to integrate inputs. For example, PaLM-E embeds robot states alongside text and images.
Why It Happened: VLMs showed Transformers could handle multiple modalities, prompting researchers to incorporate actions for real-world applications like robotics or gaming, where language guides physical or virtual tasks.
8. Segment Anything Model (~2023, Adapted to Multimodal Contexts by 2025)
What Is It?
The Segment Anything Model (SAM, Kirillov et al., 2023) is primarily a vision model for segmenting objects in images, but its relevance to NLP grows in multimodal systems by 2025, where it integrates with language models for tasks like image-based question answering or scene understanding.
How It Works
Architecture: SAM uses a Vision Transformer (ViT) backbone with a promptable segmentation head. In multimodal contexts, it pairs with a language model (e.g., a BERT-like encoder) to process text prompts for segmentation.
Training: Pre-trained on 1B images with 11M segmentation masks (SA-1B dataset) using self-supervised learning. Multimodal versions fine-tune with text-image pairs to align segmentation with language instructions.
Output: Generates pixel-level segmentations or answers questions about image regions based on text prompts (e.g., “Segment the cat in the image”).
Key Paper: Kirillov et al., 2023 (SAM): “A promptable segmentation model for zero-shot generalization.”
How It Evolved from Language Action Models
Technical Transition: SAM’s vision-focused architecture built on ViT, which itself was inspired by the Transformer’s attention mechanism. In multimodal NLP contexts, SAM integrates with language models (similar to VLMs and LAMs), using cross-modal attention to align text prompts with visual segmentations. The evolution involved extending LAMs’ action-oriented multimodal processing to include fine-grained visual tasks.
Why It Happened: LAMs showed that Transformers could bridge language and real-world tasks. SAM’s segmentation capabilities extended this to precise visual understanding, driven by the demand for interactive AI systems that combine language, vision, and action.
Technical Evolution: How One Model Led to Another
The evolution from Masked Language Models to the Segment Anything Model reflects a progression in Transformer-based architectures, driven by the foundational work in Attention Is All You Need (2017):
Masked Language Model (BERT): Leveraged the Transformer’s encoder for bidirectional context, introducing pre-training with MLM and NSP. This established the power of unsupervised learning on large corpora.
Language Comprehension Model (RoBERTa): Refined BERT’s pre-training by scaling data and optimizing tasks, improving efficiency and performance within the same encoder framework.
Large Language Model (GPT-3, T5): Scaled up Transformer architectures (decoder-only or encoder-decoder) to billions of parameters, using autoregressive or text-to-text pre-training for general-purpose tasks, building on BERT’s pre-training insights.
Small Language Model (DistilBERT): Addressed LLMs’ computational costs via distillation, compressing Transformer layers while retaining performance, adapting the same attention mechanisms.
Mixture of Experts (Switch Transformer): Introduced sparsity to LLMs, adding a gating mechanism to the Transformer to activate subsets of parameters, enhancing scalability and efficiency.
Vision-Language Model (CLIP): Extended the Transformer to multimodal tasks, pairing text and image encoders with contrastive learning, building on LLM and MoE modularity.
Language Action Model (PaLM-E): Incorporated action spaces into multimodal Transformers, using cross-modal attention to align language with physical or virtual actions.
Segment Any Model (SAM): Adapted ViT (a Transformer derivative) for vision tasks, integrating with language models in multimodal systems to enable precise, prompt-driven segmentation.
Core Technical Driver: The Transformer’s self-attention mechanism, introduced by Vaswani et al., enabled parallel processing and long-range dependency modeling. Each model extended this by scaling parameters, optimizing pre-training, adding sparsity, or incorporating new modalities (vision, actions), driven by advances in compute, data, and task demands.
Wrapping Up
The journey from Masked Language Models like BERT to multimodal systems like the Segment Anything Model showcases the Transformer’s versatility. Starting with bidirectional comprehension, NLP evolved through scaled-up models, efficient variants, and multimodal integrations, culminating in systems that blend language, vision, and action. Each step built on the last, leveraging the Transformer’s attention mechanism to tackle increasingly complex tasks. By 2025, these models power everything from chatbots to robots, proving that the evolution of NLP is really a story of adapting one brilliant idea—attention—to the world’s diverse needs.