Encoder-only transformers
Architectures utilizing only the encoder component of the Transformer architecture, characterized by bidirectional self-attention. Unlike decoder-only models, these models process the entire input sequence simultaneously, allowing each token to attend to both preceding and following tokens in the context.
Core Functionality & Use Cases
Primary applications are focused on discriminative, extractive, and sequence-labeling tasks within natural-language-processing (NLP):
Comparative Context
- Attention Mechanism: Uses bidirectional context, whereas Decoder-only models (e.g., gemini, GPT) use causal/masked self-attention to prevent looking “ahead” in the sequence.
- Task Specialization: While encoder-only models excel at understanding and labeling, generative large-language-models (LLMs) are optimized for autoregressive text generation.
- Emerging Trends in Extraction:
- New developments like LangExtract (Google) leverage generative gemini models to perform Information Extraction from unstructured text.
- This represents a shift from traditional NLP pipelines toward using generative power for specific, non-generative extraction tasks, despite the inherent challenges of using large-scale generative models for structured tasks.
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- 2026 04 14 Langextract Sam Witteveen