The evolution of large language models (LLMs) builds upon the foundations laid by two seminal papers in natural language processing (NLP): “Attention is All You Need” and the “word2vec” paper. These papers introduced key concepts and techniques that have shaped the development of LLMs.
The paper “Attention is All You Need,” published in 2017 by Vaswani et al., presented the Transformer architecture, which revolutionized sequence modeling tasks such as machine translation. The Transformer model introduced the concept of self-attention mechanisms, allowing the model to weigh the importance of different words in a sentence when processing them. This attention mechanism enabled the model to capture long-range dependencies and contextual relationships between words effectively. The Transformer’s attention mechanism became a fundamental building block for subsequent advancements in NLP, including LLMs.
The “word2vec” paper, published in 2013 by Mikolov et al., proposed efficient algorithms to learn distributed representations of words, aka word embeddings. The word2vec models aimed to capture semantic and syntactic similarities between words by mapping them to continuous vector representations in a high-dimensional space. This representation enabled models to perform various NLP tasks by leveraging the learned word embeddings. The success of word2vec showcased the potential of unsupervised learning for NLP and inspired further research in developing more advanced language models.
The evolution of LLMs can be viewed as a progression that builds on the key ideas from among other seminal papers, these two pivotal papers. Initially, models like the Generative Pre-trained Transformer focused on pre-training language representations using unsupervised learning on vast amounts of text data. These models, including GPT-2 and GPT-3, employed a Transformer architecture enhanced with the self-attention mechanism introduced in “Attention is All You Need.” By training on diverse text sources, these models learned to generate coherent and contextually relevant text in various tasks such as text completion, summarization, and even creative writing.
LLMs extended the concept of word embeddings introduced by word2vec to larger contextualized embeddings. Instead of representing each word as a fixed vector, LLMs learned contextual embeddings that captured the meaning of words within the context of a given sentence or document. This contextualization allowed LLMs to produce more nuanced and accurate representations, enabling them to understand and generate text that exhibits contextual coherence and semantic understanding.
By combining self-attention mechanisms with large-scale pre-training on diverse text corpora and contextualized word embeddings, LLMs have significantly advanced the state-of-the-art in NLP, enabling a wide range of applications and driving further progress in natural language understanding and generative AI tasks.