TransformerBased

Transformer-based models are neural networks that rely on the Transformer architecture, first introduced in the 2017 paper Attention Is All You Need. They use self-attention mechanisms to compute token representations in parallel, enabling efficient processing of long-range dependencies and large contexts compared with earlier recurrent architectures.

The core components of the Transformer include multi-head self-attention, position-wise feed-forward networks, residual connections, layer normalization, and learned positional encodings to inject order information. Models are often categorized by their architecture: encoder-only (for example BERT), decoder-only (for example GPT), and encoder-decoder (for example T5, BART). Training objectives align with the architecture: masked language modeling for encoder models, autoregressive language modeling for decoder models, and sequence-to-sequence objectives for encoder-decoder models.

Transformer-based models are used across natural language processing, machine translation, summarization, and question answering, and have extended to other domains such as computer vision with vision transformers and multimodal systems that fuse text, images, and audio. The common approach is pretraining on large unlabeled corpora and fine-tuning for downstream tasks, or using few-shot and instruction-following methods with larger models.

Advantages include strong performance on a wide range of tasks, transfer learning, and the ability to scale with data and compute. Challenges include substantial computational and memory requirements, data biases, interpretability issues, and concerns about environmental impact. Ongoing research seeks to improve data efficiency, model robustness, and efficient architectures (for example, sparse attention and distillation) to address these limitations.