How Encoders Allow LLMs to Process Prompts

In this lesson, we’ll explore how transformer encoders enable large language models (LLMs) to interpret prompts and generate accurate responses.

The image is a diagram of an encoder-decoder architecture, showing the flow of input text through tokenization, embeddings, and multiple layers of attention and normalization in both the encoder and decoder sections.

Key topics covered in this article:

The critical role of encoders in NLP
Core capabilities of transformer encoders
Encoder vs. decoder architecture comparison
How GPT-style models handle prompts
BERT vs. GPT prompt encoding
Common NLP tasks powered by encoders
Advantages and limitations of encoder-based LLMs

The Importance of Encoders

Transformer encoders convert raw text into rich, contextual embeddings:

Contextual Understanding
Self-attention lets the model examine all tokens together, capturing local and long-distance dependencies.
Dynamic Embeddings
Each token’s vector reflects its meaning in context, improving downstream predictions.
Parallel Processing
Entire sequences are processed at once, accelerating training and inference compared to RNNs.

For example, in “The cat, which was sitting on the roof, jumped down,” the encoder directly links “cat” with “jumped,” despite the intervening phrase.

The image lists four capabilities of encoders: understanding context in language, handling long-range dependencies, contextual embeddings, and parallel processing and efficiency.

Encoders generalize across tasks—classification, translation, summarization—making them indispensable in modern AI.

Encoder vs. Decoder Architectures

While both use self-attention, feed-forward layers, and normalization, they differ in purpose:

Feature	Encoder	Decoder
Main Function	Embed input for understanding tasks	Generate new text token by token
Attention Mask	Full attention across all tokens	Causal (masked) attention to enforce order
Cross-Attention Layer	N/A	Attends over encoder outputs
Context Direction	Bidirectional	Unidirectional (left-to-right)

The image is a diagram comparing encoders and decoders in a neural network, showing the flow of data through components like multi-head attention, fully connected networks, and normalization layers. It illustrates the process of transforming input text into embeddings with positional encoding.

How GPT Models Process Prompts

Generative Pre-trained Transformers (GPT) use a decoder-only architecture to produce context-aware text:

Tokenization & Embedding
Split the prompt into tokens and map each to a vector.
Masked Self-Attention
Ensure each token attends only to previous ones for causal generation.
Autoregressive Decoding
Predict one token at a time, appending each new token to the context.
Output
Generate a coherent, contextually relevant sequence.

Note

Despite lacking an encoder stack, GPT’s masked attention effectively captures context for high-quality text generation.

The image is a diagram explaining how GPT models process prompts, focusing on context, relationships, and meaning using transformer architecture. It shows the flow from user prompt through encoder and decoder to generate context-aware and meaningful output.

Encoding Prompts: BERT vs. GPT

Aspect	BERT (Encoder-Only)	GPT (Decoder-Only)
Architecture	Transformer encoder	Transformer decoder
Context Direction	Bidirectional	Left-to-right
Ideal Use Case	Classification, QA, token tagging	Text generation, completion, dialogue
Processing Method	All tokens simultaneously	Sequential, autoregressive predictions

BERT excels at understanding tasks: “What is the capital of France?” → Embedding leads to “Paris.”
GPT is optimized for generation: “Write a story about a dragon” → Narrative unfolds token by token.

Handling Long-Range Dependencies

Encoders naturally capture relationships between distant words.
For instance, in “The book that I bought last week is on the table,” the encoder links “book” to “on the table,” regardless of the intervening words.

Encoder Applications in NLP Tasks

Text Classification

Convert input sentences into embeddings for classifiers (e.g., sentiment analysis).

Question Answering

Encode both question and passage to pinpoint correct answers.

Summarization

Process long documents into embeddings that extract key information for concise summaries.

The image is about summarization, showing a clipboard icon and text explaining that encoders process lengthy documents to generate embeddings capturing core ideas.

Translation

In models like T5, the encoder transforms source text embeddings that the decoder uses to generate the target language (e.g., “The cat is on the roof” → “Le chat est sur le toit”).

Benefits of Encoders in LLMs

The image lists the benefits of encoders in large language models, highlighting rich contextual understanding, handling long inputs, high effectiveness, and efficiency and scalability.

Rich contextual embeddings
Efficient handling of long sequences
Parallelized computation for speed and scalability
Flexible features for diverse downstream tasks

Challenges of Encoders in LLMs

Warning

Encoder-based LLMs require substantial computational resources and memory during training and inference.

The image lists challenges of encoders in large language models, including computational power requirements, handling long inputs, self-attention mechanisms, pre-training needs, and limited processing ability without pre-training.

Self-attention scales quadratically with sequence length
Pretraining demands large datasets and high compute
Performance degrades without extensive pretraining

Links and References

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