Transformervarianter

Transformervarianter is a broad term used in contemporary AI literature to describe the family of models built around the transformer architecture. It encompasses encoder-only, decoder-only, and encoder-decoder designs, as well as variants that modify attention patterns, training objectives, or modalities. The term is sometimes used in Nordic and European research contexts to refer to transformer variants as a collective category.

The concept arose from the original Transformer (Vaswani et al., 2017), which introduced self-attention and a stack of feed-forward layers. Since then, variants such as BERT (encoder), GPT (decoder), T5 (text-to-text), and Vision Transformer have expanded capabilities across languages, code, audio, and vision. More recent work focuses on efficiency and long-range context through sparse attention and memory-augmented designs.

Core features across transformervarianter include multi-head self-attention, positional encoding, residual connections, and layer normalization. Variants differ by attention mechanism (dense vs sparse), sequence length handling, parameter sharing, and training objectives (masked language modeling, autoregressive generation, or denoising).

Common subtypes include encoder-only models for representation learning, decoder-only models for generation, and encoder-decoder models for sequence-to-sequence tasks. Multimodal transformervarianter integrate text with images or other data. Efficiency-oriented variants pursue faster inference and lower memory use via long-range attention, reversible layers, kernelized attention, or compression techniques.

Applications span natural language processing, computer vision, multi-modal tasks, and research for code or biology. Challenges include data requirements, bias and safety concerns, interpretability, and environmental impact of large-scale pretraining.

See also: Transformer; BERT; GPT; T5; ViT; Longformer; Reformer.