Encoder Only, Encoder Decoder, And Decoder Only Models

In natural language processing (NLP), transformer-based models have revolutionized how machines understand and generate human language. These models typically fall into three main categories based on their architecture and purpose: encoder-only, encoder-decoder, and decoder-only models. Each serves a unique role in processing language. Encoder-only models are designed primarily for understanding input text and are widely used for classification and analysis tasks. Encoder-decoder models handle input-to-output transformations, making them ideal for tasks like translation and summarization. Decoder-only models focus on generating text, often used in applications such as chatbots, code generation, and content creation. Understanding these model types is key to selecting the right architecture for specific NLP tasks.

🔹 Encoder-Only Models

Encoder-only models are designed to understand input text by converting it into rich, contextual representations. These models look at the entire input sequence simultaneously and learn the relationships between all tokens. They’re ideal for tasks like classification, named entity recognition, and semantic similarity. A well-known example is BERT, which reads the text in both directions to capture deep contextual meaning.

✅ Purpose: Understanding and encoding input text.

📘 Examples:

• BERT (Bidirectional Encoder Representations from Transformers)

• RoBERTa (A robustly optimized BERT pretraining approach)

• DistilBERT (Smaller, faster version of BERT)

🧠 Training Methodology:

Masked Language Modeling (MLM): During training, some words in the input are randomly masked (e.g., “The cat sat on the [MASK]”) and the model is trained to predict them.

Next Sentence Prediction (BERT only): Trained to predict whether two sentences follow each other in context.

BERT is trained on large corpora like Wikipedia and BookCorpus, using a bidirectional attention mechanism, so it learns context from both left and right of a word.

🔹 Encoder-Decoder Models

Encoder-decoder models handle sequence-to-sequence tasks, where an input sequence needs to be transformed into a different output sequence — such as translating one language into another or summarizing a document. The encoder processes the input and compresses it into a context vector, while the decoder generates the output based on that vector and previous outputs. Models like T5 and BART follow this architecture.

✅ Purpose: Input → Output transformation (translation, summarization, etc.)

📘 Examples:

• T5 (Text-to-Text Transfer Transformer)

• BART (Bidirectional and Auto-Regressive Transformers)

• MarianMT (for machine translation)

• mBART (multilingual BART)

🧠 Training Methodology:

Denoising Autoencoding (BART): The input text is corrupted (e.g., some words are removed or shuffled), and the model is trained to reconstruct the original.

Text-to-Text (T5): All NLP tasks are reformulated as a text-to-text problem, e.g., Input: “Translate English to French: The house is big” Output: “La maison est grande”

Cross-Attention: The decoder pays attention to encoder outputs while generating predictions.

These models are trained on massive datasets like Common Crawl, C4, and multilingual corpora.

🔹 Decoder-Only Models

Decoder-only models are built for generating text based on a prompt. They take in a partial sequence and predict the next token one step at a time, using previous tokens as context. These models are trained with causal masking to ensure they don’t “peek” at future words. The GPT family (like GPT-3 and GPT-4) are prime examples, widely used for tasks like storytelling, code generation, and conversational AI.

✅ Purpose: Generate text based on a prompt.

📘 Examples:

• GPT-2, GPT-3, GPT-4 (Generative Pretrained Transformer)

• GPT-Neo / GPT-J

• LLaMA / LLaMA 2

• Mistral (open-weight models)

🧠 Training Methodology:

Causal Language Modeling (CLM): Trained to predict the next token given all previous tokens. For example: Input: “The cat sat on the” Target: “mat”

Uses unidirectional (left-to-right) attention to prevent the model from seeing future tokens.

Fine-tuning or instruction tuning is often applied afterward (e.g., with Reinforcement Learning from Human Feedback — RLHF — in ChatGPT).

GPT models are trained on a mixture of internet text, books, code, and conversations, with emphasis on scaling laws (larger data, longer context, more parameters).