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What is BERT and how does it work
4 cited papers · May 24, 2026 · Powered by Researchly AI
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TL;DR
BERT (Bidirectional Encoder Representations from Transformers) is a landmark pre-trained language model designed to learn deep bidirectional representations fro…
**BERT1** (Bidirectional Encoder Representations from Transformers) is a landmark pre-trained language model designed to learn deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. Devlin et al. (2019) BERT is built upon the Transformer architecture, which is based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Vaswani et al. (2017)2
- Transformer Architecture — The foundational network architecture based solely on attention mechanisms, forming the backbone of BERT's encoder stack.
- Masked Language Modeling (MLM) — BERT's core pre-training objective, where tokens in a sentence are randomly masked and the model learns to predict them using bidirectional context.
Devlin et al. (2019)
- Bidirectional Contextual Representations — Unlike unidirectional models, BERT conditions on both left and right context simultaneously across all layers, enabling richer semantic understanding.
- Fine-tuning for Downstream Tasks — After pre-training, BERT is fine-tuned for specific Natural Language Understanding (NLU) tasks such as question answering and sentence classification. Liang & Liang (2024)
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Diagram
INPUT LAYER ─────────────────────────────────────────────────── Raw Text: "The cat sat on the [MASK]" │ ▼ ┌─────────────────────────┐ │ Tokenization │ │ (WordPiece Tokenizer) │ └────────────┬────────────┘ │ ▼ ┌─────────────────────────────────────────┐ │ Input Embeddings │ │ Token Emb + Segment Emb + Position Emb │ │ [CLS] token1 token2... [MASK] [SEP] │ └────────────────┬────────────────────────┘ │ PRE-TRAINING ENCODER STACK ─────────────────────────────────────────────────── ▼ ┌────────────────────────────────────┐ │ Transformer Encoder Layer 1 │ │ ┌──────────────────────────────┐ │ │ │ Multi-Head Self-Attention │ │ │ │ (attends LEFT + RIGHT) │ │ │ └──────────────┬───────────────┘ │ │ │ Add & Norm │ │ ┌──────────────▼───────────────┐ │ │ │ Feed-Forward Network (FFN) │ │ │ └──────────────────────────────┘ │ │ │ Add & Norm │ └─────────────────┼──────────────────┘ │ (×N layers, e.g. N=12 for BERT-Base) │ ▼ ┌────────────────────────────────────┐ │ Transformer Encoder Layer N │ │ (same structure as Layer 1) │ └────────────────┬───────────────────┘ │ OUTPUT LAYER ─────────────────────────────────────────────────── ▼ ┌─────────────────────────────────────┐ │ Contextual Token Representations │ │ H_1, H_2,..., H_n (hidden dim) │ └──────────┬──────────────────────────┘ │ ┌───────┴────────┐ ▼ ▼ ┌─────────┐ ┌──────────────────┐ │ [CLS] │ │ Masked Token │ │ vector │ │ Prediction Head │ │ (for │ │ (Softmax over │ │ classif)│ │ vocabulary) │ └────┬────┘ └──────────────────┘ │ FINE-TUNING ─────────────────────────────────────────────────── ▼ ┌──────────────────────────────────────┐ │ Task-Specific Head │ │ (e.g. NLU, QA, NER, Classification)│ └──────────────────────────────────────┘
BERT's pre-training uses Masked Language Modeling, where tokens are randomly masked and the model predicts them using the full bidirectional context of the sentence. A key limitation of MLM, however, is that it neglects dependency among predicted tokens — a problem that later models like MPNet attempted to address by combining permuted language modeling with auxiliary position information.12Song et al. (2020)1
Furthermore, research has shown that BERT sentence embeddings without fine-tuning tend to induce a non-smooth, anisotropic semantic space, which can harm performance on semantic similarity tasks. Li et al. (2020)
1
MPNet: Masked and Permuted Pre-training for Language UnderstandingKaitao Song, Xu Tan et al.2020arXiv (Cornell University)
View 2
MaBERT:A Padding Safe Interleaved Transformer Mamba Hybrid Encoder for Efficient Extended Context Masked Language ModelingJinwoong Kim, Sangjin Park2026Semantic Scholar
Table
| Feature | Description |
|---|---|
| Architecture | Transformer Encoder (bidirectional) |
| Pre-training Objective | Masked Language Modeling (MLM) |
| Context | Left + Right simultaneously |
| Usage | Pre-train → Fine-tune on NLU tasks |
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BERT's sentence embeddings without fine-tuning poorly capture the semantic meaning of sentences due to an anisotropic embedding space. Additionally, BERT's self-attention mechanism scales quadratically with sequence length, making long-context modeling computationally expensive.1
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MaBERT:A Padding Safe Interleaved Transformer Mamba Hybrid Encoder for Efficient Extended Context Masked Language ModelingJinwoong Kim, Sangjin Park2026Semantic Scholar
- BERT pre-trains deep bidirectional representations by jointly conditioning on both left and right context in all layers.
- The Transformer's attention-only architecture is the foundational backbone that makes BERT's bidirectional encoding possible.
- BERT is typically pre-trained with MLM and then fine-tuned for specific NLU tasks without altering the encoder architecture.
- BERT's MLM objective neglects dependency among predicted tokens, a known limitation addressed by subsequent models.
- Without fine-tuning, BERT embeddings form a non-smooth anisotropic space that limits semantic similarity performance.
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- "BERT vs RoBERTa vs ALBERT pre-training improvements comparison"
- "Fine-tuning BERT for text classification and named entity recognition"
- "Transformer encoder architecture multi-head self-attention mechanism explained"
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