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Compare BERT, GPT, T5

7 cited papers · May 26, 2026 · Powered by Researchly AI

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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 T5.¹²

Diagram

Each of these models adapts this foundation for a distinct pre-training paradigm, leading to different strengths across NLP tasks.

²Devlin et al. (2019)²

1

Attention Is All You NeedAshish Vaswani, Noam Shazeer et al.2017Advances in Neural Information Processing Systems (NeurIPS)

2

BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingJacob Devlin, Ming-Wei Chang et al.2019NAACL-HLT 2019

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.

¹

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.

²³Devlin et al. (2019)²

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.

⁴Radford et al. (2018)⁴

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)

1

Attention Is All You NeedAshish Vaswani, Noam Shazeer et al.2017Advances in Neural Information Processing Systems (NeurIPS)

2

BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingJacob Devlin, Ming-Wei Chang et al.2019NAACL-HLT 2019

3

MPNet: Masked and Permuted Pre-training for Language UnderstandingKaitao Song, Xu Tan et al.2020arXiv (Cornell University)

4

Improving Language Understanding by Generative Pre-TrainingAlec Radford, Karthik Narasimhan et al.2018OpenAI Blog

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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

1

Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann et al.2020Advances in Neural Information Processing Systems (NeurIPS)

2

Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerColin Raffel, Noam Shazeer et al.2020Journal of Machine Learning Research

3

BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingJacob Devlin, Ming-Wei Chang et al.2019NAACL-HLT 2019

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BERT produces sentence embeddings that poorly capture semantic meaning without fine-tuning, inducing a non-smooth anisotropic semantic space that harms semantic similarity performance.

¹²Li et al. (2020)¹

GPT relies on unidirectional (left-to-right) language modeling, which limits its ability to leverage full bidirectional context; BERT's MLM addresses dependency among predicted tokens that GPT-style models neglect.

²

Song et al. (2020)

1

On the Sentence Embeddings from Pre-trained Language ModelsBohan Li, Hao Zhou et al.2020OpenAlex

2

BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingJacob Devlin, Ming-Wei Chang et al.2019NAACL-HLT 2019

The Transformer's attention-only architecture is the shared foundation enabling parallelizable training for all three models.

¹²³⁴

BERT's bidirectional pre-training via MLM allows fine-tuning for diverse NLU tasks with minimal architectural changes.

²Devlin et al. (2019)²

GPT-3 demonstrates that scaling language models to 175 billion parameters enables strong few-shot task performance without any fine-tuning.

⁵Brown et al. (2020)⁵

T5 unifies all NLP tasks into a single text-to-text framework, achieving state-of-the-art results across summarization, QA, and classification.

⁴Raffel et al. (2020)⁴

BERT's sentence embeddings suffer from anisotropy in semantic space, highlighting that pre-trained representations require careful adaptation for similarity tasks.

²

Li et al. (2020)

1

Attention Is All You NeedAshish Vaswani, Noam Shazeer et al.2017Advances in Neural Information Processing Systems (NeurIPS)

2

BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingJacob Devlin, Ming-Wei Chang et al.2019NAACL-HLT 2019

3

Improving Language Understanding by Generative Pre-TrainingAlec Radford, Karthik Narasimhan et al.2018OpenAI Blog

4

Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerColin Raffel, Noam Shazeer et al.2020Journal of Machine Learning Research

5

Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann et al.2020Advances in Neural Information Processing Systems (NeurIPS)

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