Large Language Models (LLMs) utilize several foundational architectures to perform tasks such as natural language understanding, generation, translation, and more. The main architectures used to build LLMs include:
1. Transformer architecture: – Description: Introduced by Vaswani et al. in the paper “Attention is All You Need” (2017), the Transformer architecture has become pivotal in the development of LLMs. It replaces recurrent layers with attention mechanisms, thereby enabling models to train faster and handle long-range dependencies more effectively. – Example: BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) models. – Source: Vaswani, A., et al. (2017). “Attention is All You Need”. NeurIPS.
1. Recurrent Neural Networks (RNNs): – Description: Before Transformers, RNNs were widely used due to their ability to process sequential data. Variants like LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) address the vanishing gradient problem, allowing for better handling of long-term dependencies. – Example: ELMo (Embeddings from Language Models), which uses bi-directional LSTMs for producing contextual word embeddings. – Source: Hochreiter, S., & Schmidhuber, J. (1997). “Long Short-Term Memory”. Neural Computation.
1. Convolutional Neural Networks (CNNs): – Description: Although more commonly associated with image processing, CNNs have been adapted to NLP tasks, especially for modeling local dependencies and extracting relevant features from text. – Example: Models that use CNNs for text classification or sentence modeling. – Source: Kim, Y. (2014). “Convolutional Neural Networks for Sentence Classification”. EMNLP.
- Transformers vs RNNs: Transformers tend to outperform RNNs in generating and understanding context over long text spans due to the self-attention mechanism, which allows parallel processing and reduces training times.
- CNNs vs RNNs/Transformers: CNNs are more efficient in capturing local dependencies and patterns, but they are generally not as powerful as RNNs and Transformers for tasks requiring understanding long-range dependencies in text.
The progression from RNNs to Transformers marks a significant evolution in LLM architectures. While RNNs and their variants like LSTM and GRU laid the groundwork for handling sequences, the advent of Transformers has revolutionized the field by enabling more efficient and scalable training of models. Each architecture has its strengths and applicable scenarios, contributing to the robust landscape of modern NLP.
- Vaswani, A., et al. (2017). “Attention is All You Need”. NeurIPS.
- Hochreiter, S., & Schmidhuber, J. (1997). “Long Short-Term Memory”. Neural Computation.
- Kim, Y. (2014). “Convolutional Neural Networks for Sentence Classification”. EMNLP.
- Devlin, J., et al. (2019). “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”. ACL.
- Brown, T. B., et al. (2020). “Language Models are Few-Shot Learners”. NeurIPS.
- Peters, M. E., et al. (2018). “Deep contextualized word representations”. NAACL.
