Encoder Model Evolution and Whether Decoder-only LLMs Can Replace Them: From BERT to ModernBERT

Forum topic · ✨步子哥 · 2026-06-28

Summary

This article reviews the development of encoder-only language models from BERT (2018) through successors like RoBERTa, ALBERT, ELECTRA, and DeBERTa, and examines whether decoder-only models (GPT, LLaMA, Qwen, DeepSeek) can replace them. It highlights ModernBERT (2024, Answer.AI/LightOn) as a major modernization: 8192-token context (16x longer than typical encoders), up to 4x faster than DeBERTa, first Base-scale model to beat DeBERTa-v3 on GLUE, state-of-the-art long-context retrieval, and strong code retrieval thanks to 2-trillion-token diverse pretraining with RoPE, GeGLU, and alternating local-global attention. The analysis argues the two architectures are complementary rather than interchangeable: encoder-only models offer bidirectional understanding, low latency, small footprint, and local deployment for classification, retrieval, and entity extraction, while decoder-only models excel at generation, zero/few-shot generalization, but at high cost—notably a FineWeb-Edu example where BERT-based filtering cost ~$60K versus over $1M with decoder APIs. RAG systems exemplify their collaboration. Conclusion: encoders remain irreplaceable for efficient understanding tasks.

Overview

This forum post analyzes the evolution of encoder-only language models and evaluates whether decoder-only large language models can fully replace them.

1. Encoder-only models: from BERT to ModernBERT

BERT (2018) introduced large-scale bidirectional pretraining via masked language modeling, achieving breakthroughs in NLU tasks (classification, NER), but cannot generate text.
Post-BERT improvements:
RoBERTa (2019): removed NSP, larger batches/data, outperformed BERT on GLUE.
ALBERT (2019): parameter sharing and embedding factorization for lighter models; SOP task.
ELECTRA (2020): generator-discriminator replaced-token-detection pretraining for efficiency.
DeBERTa (2021) / DeBERTa-v3: disentangled attention and relative positions; top accuracy but larger and slower.
ModernBERT (late 2024, Answer.AI & LightOn et al.) — a Pareto improvement in speed, accuracy, and context length:
Native 8192-token context (16x the usual 512).
Up to 4x faster than DeBERTa on variable-length input; ~3x faster at long-context inference; under one-fifth the memory.
First Base-scale model to beat DeBERTa-v3 on GLUE; SOTA on long-context retrieval (ColBERT-style, +9 points); strong code retrieval.
Architecture: RoPE, GeGLU, no unnecessary biases, extra normalization, alternating local (128-token window) and global attention (global every 3 layers).
Trained on 2 trillion tokens of diverse data (web, code, scientific articles), mostly unique.

2. Decoder-only models

Representative models: GPT series, LLaMA, Qwen, DeepSeek. Autoregressive next-token prediction enables strong generation, zero/few-shot in-context learning, parallel training with causal masking, and efficient KV-Cache inference.
Limitations: huge parameter counts (e.g., LLaMA-3.1 405B) make deployment expensive; unidirectional attention limits understanding; often less cost-effective than fine-tuned BERT-style models for classification/retrieval; hard to run locally.

3. Can they replace each other?

Task fit: encoders excel at deep input understanding (retrieval, classification, entity extraction) with low latency and cost; decoders excel at generation and cross-task generalization.
Cost example: for FineWeb-Ede quality filtering of 15 trillion tokens, an Llama-3-70b + fine-tuned BERT pipeline cost ~$60K (6,000 H100s for 6,000 hours); doing it entirely with decoder APIs (even Gemini Flash) would exceed $1 million.
Synergy: RAG systems pair encoder-based retrieval with decoder-based generation. Research is exploring unified/hybrid attention architectures.

Conclusion

Encoder-only models, revitalized by ModernBERT, are not obsolete: decoder-only LLMs dominate generation but cannot fully replace encoders in efficiency-critical understanding and retrieval workloads. The two architectures are complementary, not substitutable, and will likely keep cooperating in future NLP systems.