Transformer-based large language models process an entire sequence of tokens at once instead of one token at a time. Which architectural feature enables this parallel computation?
Gradient clipping
Data parallelism
Self-attention mechanism
Long short-term memory (LSTM) cells
Transformers replace the recurrence found in RNNs with self-attention layers. Self-attention lets the model compute relationships between every pair of tokens simultaneously, so the whole sequence can be processed in parallel on GPU hardware. Gradient clipping is a training safeguard, LSTM cells belong to recurrent networks, and data parallelism is a high-level training strategy rather than an internal model component. None of these alternatives provide the core parallel-sequence capability that distinguishes transformer LLMs.
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