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The evolution of LLMs has been shaped by several significant research milestones over the past few decades. This chapter will provide an overview of these foundational studies, highlighting their contributions to the advancement of natural language processing (NLP) techniques and architectures used in contemporary models. We begin by discussing early efforts in statistical machine translation (SMT), followed by seminal work in neural network approaches for sequence-to-sequence (Seq2Seq) tasks. Subsequently, we delve into attention mechanisms, which revolutionized NLP model performance before culminating in the groundbreaking transformer architecture.

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Building Up to Large Language Models (LLMs)

  • 章节

  • 发表日期：

    2025年

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  • Vikram Dhillon,
  • David Metcalf,
  • Max Hooper

摘要

The evolution of LLMs has been shaped by several significant research milestones over the past few decades. This chapter will provide an overview of these foundational studies, highlighting their contributions to the advancement of natural language processing (NLP) techniques and architectures used in contemporary models. We begin by discussing early efforts in statistical machine translation (SMT), followed by seminal work in neural network approaches for sequence-to-sequence (Seq2Seq) tasks. Subsequently, we delve into attention mechanisms, which revolutionized NLP model performance before culminating in the groundbreaking transformer architecture.