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In recent years, Generative AI has surpassed Discriminative AI in both capability and prominence, largely due to the emergence of the Transformer architecture. This breakthrough has revolutionized natural language processing (NLP), enabling widely adopted tools like ChatGPT and Claude. The impact of Transformers on AI parallels that of the internet or aviation in transforming society. Despite their importance, Transformers remain difficult to understand for many, owing to their technical complexity and the high computational resources needed for experimentation. This limits accessibility for learners and practitioners outside elite research institutions. This paper aims to demystify Transformer models by offering intuitive explanations and analogies that clarify their core components—such as self-attention and feed-forward layers. Additionally, we present a practical implementation of a small language model (SLM) using Keras, demonstrating its application in tasks like Neural Machine Translation (NMT) and IMDb sentiment classification. Through this approach, we provide both a conceptual and hands-on foundation to make Transformer models more accessible and transparent.

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Transformers: From Black Box to Glass Box - Demystifying Their Architecture for NLP

  • 会议论文

  • 发表日期：

    2026年

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  • N. S. Surya,
  • Ramnaresh Ulaganathan,
  • B. Aravindh,
  • Kona Deepak,
  • M. Anbazhagan

摘要

In recent years, Generative AI has surpassed Discriminative AI in both capability and prominence, largely due to the emergence of the Transformer architecture. This breakthrough has revolutionized natural language processing (NLP), enabling widely adopted tools like ChatGPT and Claude. The impact of Transformers on AI parallels that of the internet or aviation in transforming society. Despite their importance, Transformers remain difficult to understand for many, owing to their technical complexity and the high computational resources needed for experimentation. This limits accessibility for learners and practitioners outside elite research institutions. This paper aims to demystify Transformer models by offering intuitive explanations and analogies that clarify their core components—such as self-attention and feed-forward layers. Additionally, we present a practical implementation of a small language model (SLM) using Keras, demonstrating its application in tasks like Neural Machine Translation (NMT) and IMDb sentiment classification. Through this approach, we provide both a conceptual and hands-on foundation to make Transformer models more accessible and transparent.