The main neural network models used in natural language processing (NLP) are convolutional neural networks (CNNs), recurrent neural networks (RNNs) and their gated variant LSTMs, encoder-decoder systems, and Transformers. They differ in how they process token sequences: CNNs focus on local patterns, RNNs carry information step by step, and Transformers use attention to connect positions directly. BERT and GPT are prominent Transformer-based model families, designed mainly for understanding and generation, respectively.
What building blocks do neural NLP models use?
Neural NLP models learn numerical representations of tokens and sequences so their layers can detect patterns useful for tasks such as classification, tagging, question answering, and text generation.
Tokenization and embeddings
Tokenization divides text into discrete units, which may be words, word pieces, or other subword units. An embedding table maps each token to a dense vector. Layers then transform those vectors into representations that capture information useful to the task. Embeddings became standard inputs for downstream tasks such as named-entity recognition, part-of-speech tagging, and question answering.
Feed-forward layers
A feed-forward network transforms its input through a sequence of learned operations. It can classify a representation or predict an output, but by itself it does not provide a mechanism for tracking order across a long sequence. CNNs, RNNs, and Transformers add different ways to model relationships among tokens.
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- NLP: The Essential Guide to Neuro-Linguistic Programming
How do CNNs, RNNs, and LSTMs process text?
CNNs: local patterns in parallel
A one-dimensional convolution scans windows of neighboring tokens, detecting short patterns similar to n-grams. Its operations can run in parallel across positions, making CNNs useful for tasks such as sentence classification and for some lightweight inference settings. A convolution sees only a local receptive field unless layers are stacked or the architecture uses mechanisms such as pooling or dilation to expand it.
RNNs: a state carried through the sequence
A recurrent neural network reads tokens in order and updates a hidden state at each step. That state carries information from earlier tokens, but the sequential computation limits parallel processing during training and can make long-distance dependencies difficult to learn.
LSTMs: gated recurrence
Long short-term memory networks (LSTMs) add gates that regulate what information to retain, overwrite, or expose from the recurrent state. These gates reduce the vanishing-gradient problem that affected plain RNNs; they do not make recurrence parallel. An LSTM can still be a reasonable choice for a compact system that processes a stream incrementally or where maintaining a small state is useful.
What is an encoder-decoder model?
An encoder-decoder system reads one sequence and generates another. The encoder builds a representation of the input; the decoder produces the output sequence. This pattern fits translation and other text-to-text tasks. Early encoder-decoder systems commonly used recurrent or convolutional components, often with attention. In the 2017 Transformer paper, Vaswani and colleagues described these as the dominant pre-Transformer approach.
What is a Transformer, and why did it replace recurrent networks in many tasks?
A Transformer uses self-attention: each token can weigh information from other positions in the sequence. Positional information supplies token order, because self-attention alone does not encode sequence position. Unlike an RNN, a Transformer does not have to update a single recurrent state token by token, so its training can parallelize work across sequence positions more effectively. Attention also provides direct paths between distant tokens, rather than requiring information to pass through every intervening recurrent step.
Vaswani and colleagues introduced the architecture in 2017, describing it as “based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.” In their reported WMT 2014 English-to-French translation experiment, the Transformer reached 41.0 BLEU after 3.5 days of training on eight GPUs. That is a result for that specific benchmark and experimental setup, not a general performance guarantee for every Transformer or translation task.
Three common Transformer designs
- Encoder-only: builds contextual representations and is suited to classification, tagging, and other language-understanding tasks.
- Decoder-only: predicts text from preceding context and is suited to generation.
- Encoder-decoder: conditions an output sequence on an input sequence, a natural fit for text-to-text transformations such as translation.
How do BERT and GPT differ?
| Model family | Architecture and training objective | Typical fit |
|---|---|---|
| BERT | Bidirectional Transformer encoder pretrained to predict masked tokens; the original formulation also used a sentence-relationship objective. | Language understanding tasks such as classification, tagging, inference, and question answering, often by fine-tuning a task-specific head. |
| GPT-style models | Autoregressive Transformer decoder with causal attention; each position predicts the next token from prior context. | Text generation and tasks that can be expressed as continuing or producing text, including use through prompting. |
BERT: bidirectional representations for understanding
BERT reads context in both directions through its encoder representation. Google Research described it as “a method of pre-training language representations” using a large text corpus, followed by adaptation to downstream tasks. Its original training included masked-token prediction and a sentence-relationship objective; a task-specific head can then be fine-tuned for tasks including question answering, inference, classification, and tagging.
In results reported by Devlin and colleagues in 2019, BERT achieved a GLUE score of 80.5, MultiNLI accuracy of 86.7%, SQuAD v1.1 test F1 of 93.2, and SQuAD v2.0 test F1 of 83.1. These figures belong to the paper’s reported evaluations and do not establish how BERT compares with later models on current benchmarks.
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GPT-style models use causal attention: a token is predicted from the tokens before it, not future tokens in the same sequence. The approach scales pretraining across model size, corpus, and computation. GPT-3-family research describes strong performance on many NLP tasks without task-specific training, including through prompting. This does not mean every prompt works reliably or that generation is inherently factual.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which NLP model should you learn or use first?
Start with the task and constraints rather than choosing a model by name. A lightweight classifier, an incremental stream processor, a question-answering system, and a text generator have different requirements.
Choose by task shape
- Short-text classification or local phrase features: consider a CNN when local patterns and parallel computation are useful.
- Streaming or compact state: consider an RNN or LSTM when sequential processing fits the system and the state can remain small.
- Classification, tagging, or contextual understanding: consider an encoder-style Transformer such as BERT, particularly when pretrained representations can be adapted to the task.
- Open-ended or prompt-driven text generation: consider a decoder-only model such as a GPT-style model.
- Translation or another input-to-output transformation: consider an encoder-decoder Transformer when the output should be conditioned on a separate input sequence.
Compare candidates against operating needs
- Task direction: Decide whether the system must understand or classify text, generate text, or transform one sequence into another.
- Context: Estimate input lengths and whether the model must connect information far apart in the sequence.
- Data and adaptation: Check whether labeled examples are available and whether prompting, fine-tuning, or task-specific training suits the project.
- Quality: Select a metric relevant to the task. Accuracy or F1 may fit classification; BLEU or ROUGE may be used for some text-generation evaluations; perplexity measures a language model’s predictive fit. Human preference and factuality may require separate evaluation.
- Efficiency: Measure latency, memory use, and throughput under the deployment conditions that matter, including the expected batch size.
- Robustness and language coverage: Test performance with domain shifts, noisy text, multilingual input, and adversarial wording where relevant.
- Operations: Account for training and inference compute, the software stack, and the team’s ability to maintain it.
Efficiency comparisons age quickly. A 2024 NeurIPS study estimated that the compute needed to reach a given language-model performance threshold halved about every eight months, with a 90% confidence interval of roughly two to 22 months. This describes a changing trend in the study’s setting, not a fixed schedule for every task, model, or deployment.
What should you remember about the model landscape?
RNNs and LSTMs process sequences recurrently; CNNs detect local patterns; encoder-decoder models map input sequences to outputs; and Transformers use attention and positional information to model token relationships. BERT is an encoder-oriented family associated with bidirectional language understanding, while GPT-style models use autoregressive decoders for next-token generation. There is no universal winner: the right choice depends on task direction, context, quality targets, efficiency limits, robustness, language coverage, and operational capacity. Exact rankings, context windows, prices, and software APIs change over time, so assess current candidates against the intended workload rather than treating architecture labels as a benchmark.
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