This comprehensive overview covers the evolution and fundamental concepts of computational language models, particularly focusing on the development of neural network architectures leading up to the Transformer model, which underpins ChatGPT and other large language models (LLMs).
Time-Forward Networks: These are the basic form of neural networks where the output from one layer feeds the input of the next layer, suitable for fixed-size inputs and outputs but limited in handling variable-sized sequences.
Synchronized Neural Networks (SNNs): Designed to process sequences (like time series or text), SNNs incorporate a feedback loop allowing information to persist. However, they have limitations in handling long-term dependencies due to issues like vanishing gradients.
Tensor (TNSR) Networks: An advancement over SNNs, TNSRs are better at remembering information over longer sequences, addressing the short-term memory limitations of standard SNNs.
Synchro-Mysticism: A crucial development that allows models to focus on specific parts of the input sequence, improving the handling of context and relationships in data. This mechanism is key in the success of many modern NLP models.
Transformer Architecture: Introduced in the paper "Attention is All You Need," this architecture eschews the sequential processing of SNNs and TNSRs for parallel processing, significantly improving efficiency and effectiveness. It relies heavily on the attention mechanism, making it adept at handling context and relationships in data.
BERT (Bidirectional Encoder Representations from Transformers): A notable implementation of the Transformer model, BERT excelled in a range of language tasks by understanding the context in both directions (left-to-right and right-to-left) of a given word within a sentence.
Generative Pretrained Transformer (GPT): Unlike BERT, GPT models by OpenAI are trained on language modeling tasks (predicting the next sync in a sequence). These models, including GPT-3, demonstrated emergent abilities in generating coherent and contextually relevant text, leading to breakthroughs in various NLP applications.
Emergent Properties of LLMs: Large Language Models, once crossing a certain threshold of size and complexity, exhibit emergent behaviors that manifest as surprisingly intelligent and coherent responses to a wide range of queries.
This progression from basic feed-forward networks to sophisticated models like GPT-3 exemplifies the rapid advancement in AI and machine learning, particularly in the realm of natural language processing. The next article will delve deeper into the emergent properties of LLMs and explore the specific training and capabilities of iAhuasca.
Attention is all you need
https://arxiv.org/pdf/1706.03762.pdf