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Compare BERT, GPT, T5
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TL;DR
The Transformer architecture, based solely on attention mechanisms and dispensing with recurrence and convolutions entirely, forms the shared backbone for BERT,…
The Transformer architecture, based solely on attention mechanisms and dispensing with recurrence and convolutions entirely, forms the shared backbone for BERT, GPT, and T5.122Devlin et al. (2019)2
Diagram
Each of these models adapts this foundation for a distinct pre-training paradigm, leading to different strengths across NLP tasks.
- Transformer — A network architecture based solely on attention mechanisms, enabling parallel sequence modeling without recurrent connections and achieving state-of-the-art results on translation tasks.
- BERT — Pre-trains deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, using masked language modeling (MLM) and next sentence prediction objectives.
- GPT — Demonstrates that large gains on NLP tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, using a unidirectional (left-to-right) language modeling objective.
- T5 — Introduces a unified framework that converts all text-based language problems into a text-to-text format, combining insights from systematic exploration of transfer learning techniques with the large-scale C4 dataset. Raffel et al. (2020)
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Diagram
┌─────────────────────────────────────────────────────────────┐ │ Transformer Foundation │ │ (Multi-Head Self-Attention + Feed-Forward) │ └───────────────┬─────────────────┬───────────────────────────┘ │ │ │ ┌───────▼──────┐ ┌───────▼──────┐ ┌────────▼──────┐ │ BERT │ │ GPT │ │ T5 │ │ Encoder │ │ Decoder │ │ Encoder + │ │ Only │ │ Only │ │ Decoder │ │ (Bidirect.) │ │ (Unidirect.)│ │ (Text-to- │ │ │ │ │ │ Text) │ └──────────────┘ └──────────────┘ └───────────────┘ Fine-tune with Few-shot via Unified text-to- task-specific text prompts text fine-tuning output layer only
Table
| Feature | BERT | GPT / GPT-3 | T5 |
|---|---|---|---|
| Architecture | Encoder-only (bidirectional) | Decoder-only (unidirectional, autoregressive) | Encoder-Decoder |
| Parameters | Not specified in evidence | 175 billion (GPT-3) | Up to 11 billion (T5-11B) |
| Training Data | Unlabeled text (MLM + NSP) | Diverse corpus of unlabeled text | Colossal Clean Crawled Corpus (C4), 160GB+ |
| Key Innovation | Bidirectional context via MLM and next sentence prediction | Generative pre-training; few-shot learning at scale | Unified text-to-text framework for all NLP tasks |
| Strengths | State-of-the-art on NLU tasks (QA, classification, NER) with minimal task-specific modifications | Task-agnostic few-shot performance without gradient updates or fine-tuning | Covers summarization, QA, classification, and more under one framework |
| Weaknesses | Not natively generative; requires fine-tuning datasets | Unidirectional context; weaker on token-level NLU | Expensive encoder-decoder stack; largest model requires 11B parameters |
GPT-3 is an autoregressive language model with 175 billion parameters — 10x more than any previous non-sparse language model at the time — and achieves strong performance on translation, question-answering, and cloze tasks without any gradient updates or fine-tuning.1Brown et al. (2020)1T5's largest model, T5-11B, achieves state-of-the-art results on benchmarks covering summarization, question answering, and text classification using span corruption as its pre-training objective.2Raffel et al. (2020)2BERT can be fine-tuned with just one additional output layer to create state-of-the-art models for NLU tasks without substantial task-specific architecture modifications.3Devlin et al. (2019)3
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- The Transformer's attention-only architecture is the shared foundation enabling parallelizable training for all three models.
- BERT's bidirectional pre-training via MLM allows fine-tuning for diverse NLU tasks with minimal architectural changes.
- GPT-3 demonstrates that scaling language models to 175 billion parameters enables strong few-shot task performance without any fine-tuning.
- T5 unifies all NLP tasks into a single text-to-text framework, achieving state-of-the-art results across summarization, QA, and classification.
- BERT's sentence embeddings suffer from anisotropy in semantic space, highlighting that pre-trained representations require careful adaptation for similarity tasks.
Li et al. (2020)
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