Language Neural Network Models (LNLM) Architecture

An Language Neural Network Models (LNLM) Architecture is a neural model architecture for implementing large neural language models.

• Context:
  • It can (typically) utilize Transformer Blocks to manage sequential data through mechanisms like self-attention, which allows the model to prioritize information from different parts of the input sequence.
  • It can (often) be scaled up to handle vast amounts of parameters and training data to improve performance in tasks such as translation, question-answering, and text generation.
  • It can range from simpler models with fewer parameters to extremely large models like GPT-3 and GPT-4, which contain billions to trillions of parameters.
  • It can employ various training strategies, including pre-training on large datasets, fine-tuning on specific tasks, and reinforcement learning from human feedback.
  • It can incorporate different forms of parallel processing architectures to optimize training time and resource usage, such as Data Parallelism, Model Parallelism, and Pipeline Parallelism.
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• Example(s):
  • a Transformer-based LLM Architecture, such as:
    • a GPT (Generative Pre-trained Transformer) Architecture, known for its autoregressive capabilities and use in generating coherent text.
    • a BERT Architecture, which uses bidirectional training to better understand the context and nuances of language.
    • a T5 (Text-to-Text Transfer Transformer) Architecture, designed to convert text-based inputs into desired text outputs using a unified text-to-text framework.
    • a RoBERTa Architecture, an optimized version of BERT that has been trained on an even larger corpus and with more data.
    • a XLNet Architecture, which integrates ideas from Transformer-XL and BERT architecture for improved performance on a range of NLP tasks.
    • a ELECTRA Architecture, which introduces a new pre-training approach called replaced token detection.
    • a Mamba LLM Architecture, tailored for specialized applications in domain-specific areas.
    • ...
  • a Large Protein Language Model Architecture.
  • a Large Software Language Model Architecture.
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• Counter-Example(s):
  • Convolutional Neural Networks (CNN) Architecture, for CNN models,
  • Recurrent Neural Networks (RNN) Architecture, for RNN models.
  • ...
• See: Transformer Model, Self-Attention Mechanism, Neural Network Architecture, Neural Machine Translation.

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References

2023

• https://github.com/Mooler0410/LLMsPracticalGuide
  • QUOTE: We build an evolutionary tree of modern Large Language Models (LLMs) to trace the development of language models in recent years and highlights some of the most well-known models, in the following figure:
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2023

• chat
  • Here are six popular LNLM (Language Neural Network Models) architectures along with a brief description of each:
    • GPT (Generative Pre-trained Transformer): This architecture is based on the transformer architecture and is used for various NLP tasks such as text generation, text classification, and question-answering. It uses a large corpus of text to pre-train the model and then fine-tunes it for specific tasks.
    • BERT (Bidirectional Encoder Representations from Transformers): This architecture is also based on the transformer architecture and is used for NLP tasks such as text classification and named entity recognition. BERT uses a bidirectional approach to encode the input text, allowing the model to capture context from both the left and the right side of a token.
    • XLNet: This architecture extends the transformer architecture and is used for NLP tasks such as text generation and classification. It is designed to be more permutation-invariant than BERT, meaning the model can handle the same input sequence in different orders.
    • RoBERTa (Robustly Optimized BERT Pretraining Approach): This architecture is an optimized version of BERT and is used for NLP tasks such as text classification and named entity recognition. RoBERTa uses a larger corpus of text and longer training times compared to BERT, resulting in improved performance.
    • ALBERT (A Lite BERT): This architecture is a lighter version of BERT, designed to be more computationally efficient. It uses parameter-reduction techniques such as factorized embedding parameterization and cross-layer parameter sharing to reduce the number of parameters in the model.
    • T5 (Text-to-Text Transfer Transformer): This architecture is based on the transformer architecture and is designed for a wide range of NLP tasks, including text generation, text classification, and question-answering. T5 uses a text-to-text format for its inputs and outputs, allowing it to be trained on a diverse range of tasks with a single model.
  • These are some of the most widely used LNLM architectures. Each of these architectures has its own strengths and weaknesses, and the choice of architecture depends on the specific NLP task and the resources available.

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