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12 SMT007 MAGAZINE I OCTOBER 2024 Transformer Architectures Transformers can derive meanings from long text sequences to understand how differ- ent words or semantic components might be related. ey can then determine how likely they are to occur in proximity to each other. e key components include attention mechanisms that focus on different parts of the input sequence when generating output, and self-attention mechanisms to process input data—allowing the model to weigh the impor- tance of different words in a sentence sequence and understand context when making predic- tions. Its feed-forward neural networks pro- cess the attention outputs to produce the final predictions. e architecture comprises an encoder- decoder structure. e encoder processes the input sequence and produces a set of contin- uous representations (embeddings), while the decoder takes the encoder's output and gener- ates the final prediction, e.g., a translated sen- tence or a continuation of text. Additionally, a multi-head attention mechanism can improve the model's ability to focus simultaneously on different parts of the input sequence. Multiple attention heads enhance the model's capacity to capture diverse linguistic patterns and rela- tionships within the data. Transformer archi- tecture also uses positional encoding to com- pensate for the lack of sequential processing and maintains information about word order. Transformer architecture facilitates effec- tive pre-training on large datasets and subse- quent fine-tuning for specific tasks. It is a key aspect of LLM development. is pre-training allows the transformer architecture to learn general language patterns while fine-tuning works on specific datasets to improve perfor- mance tasks. Many iterations are required for a model to reach the point where it can produce plausible results. e mathematics and coding that go into creating and training generative AI models, particularly LLMs, can be incredibly time-intensive, costly, and complex. One of the unique advantages of transformer architecture is that it can handle input data in parallel. Parallel processing offers greater effi- ciency and scalability compared to other archi- tectures, such as a recurrent neural network (RNN) or long short-term memory (LSTM), which process data sequentially. LLMs Based on the concept of transformer archi- tecture, LLMs consist of intricate neural net- works trained on large quantities of unlabeled text. An LLM breaks the text into words or phrases and assigns a number to each, using sophisticated computer chips and neural net- works to find patterns in the pieces of text through mathematical formulas, and learns to "guess" the next word in a sequence. en, using NLP, the model can understand what's being asked and reply. Because it uses mathe- matical formulas rather than text searching to generate responses, it is not ready-made infor- mation waiting to be retrieved. Rather, it uses billions or even trillions of numbers to calcu- late responses from scratch; producing new sequences of words on the fly. However, LLMs are computationally intensive, requiring high computing power and parallel computing, such as graphic processing units (GPUs). LLMs are characterized by their large param- " e architecture comprises an encoder-decoder structure. "