Decoder-Only Transformer-based Neural Language Model
A Decoder-Only Transformer-based Neural Language Model is a Transformer-based neural LM that exclusively employs the decoder component of the transformer architecture in language modeling.
- Context:
- It can (typically) generate coherent and contextually relevant text in applications such as chatbots, automated content creation, and language translation.
- It can have Emergent LM Properties, such as: sentiment analysis, summarization, and question answering.
- It can range from being a Small Decoder-Only Transformer-based Neural Language Model to being a Large Decoder-Only Transformer-based Neural Language Model.
- It can be effective in Long Text Sequence Generation Tasks (tasks that require the generation of long sequences of text) due to its ability to maintain context over longer passages.
- It can be fine-tuned on domain-specific data to improve its performance in specialized areas such as legal, medical, or technical language generation.
- It can (typically) utilize a large autoregressive language model for few-shot predictions, predicting the next token in a sequence based on the previous tokens.
- It can (often) be trained on a vast corpus of text data to predict the next word in a sentence, taking into account all the previous words, thus enabling it to generate text that is contextually relevant.
- It can (often) exhibit zero-shot generalization capabilities, meaning it can perform tasks it wasn't explicitly trained to do, by understanding the task instructions given in natural language.
- It has (often) demonstrated strong performance in natural language processing tasks, surpassing recurrence and convolution-based architectures through the use of attention mechanisms that allow it to focus selectively on segments of input text it deems most relevant.
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- Example(s):
- GPT-1, one of the first.
- GPT-3, a large-scale decoder-only transformer-based model known for its text generation capabilities.
- GPT-4, an advanced iteration of GPT-3 with more layers and parameters, used for sophisticated language tasks.
- Turing-NLG, another example of a decoder-only transformer-based language model, known for its large number of parameters and deep learning capabilities.
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- Counter-Example(s):
- BERT LM, which is an encoder-only transformer-based neural language model, primarily used for understanding language rather than generating it.
- ElMO LM, which is not a transformer-based model but rather relies on recurrent neural network architecture for language modeling.
- T5, an encoder-decoder model that is designed for a wide range of NLP tasks including translation, summarization, and question-answering.
- See: Transformer Architecture, Neural Language Model, Language Generation, Pre-trained Language Models, Fine-Tuning in Machine Learning, Natural Language Processing Systems, Sequence Generation, Attention Mechanism, Autoregressive Model, Few-Shot Learning, Zero-Shot Learning.
References
2024a
- (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture) Retrieved:2024-2-18.
- A transformer is a deep learning architecture based on the multi-head attention mechanism, proposed in a 2017 paper "Attention Is All You Need". It has no recurrent units, and thus requires less training time than previous recurrent neural architectures, such as long short-term memory (LSTM), and its later variation has been prevalently adopted for training large language models on large (language) datasets, such as the Wikipedia corpus and Common Crawl.
Input text is split into n-grams encoded as tokens and each token is converted into a vector via looking up from a word embedding table. At each layer, each token is then contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism allowing the signal for key tokens to be amplified and less important tokens to be diminished. The transformer paper, published in 2017, is based on the softmax-based attention mechanism was proposed by Bahdanau et. al. in 2014 for machine translation, and the Fast Weight Controller, similar to a transformer, was proposed in 1992.
This architecture is now used not only in natural language processing and computer vision, but also in audioand multi-modal processing. It has also led to the development of pre-trained systems, such as generative pre-trained transformers (GPTs) and BERT(Bidirectional Encoder Representations from Transformers).
- A transformer is a deep learning architecture based on the multi-head attention mechanism, proposed in a 2017 paper "Attention Is All You Need". It has no recurrent units, and thus requires less training time than previous recurrent neural architectures, such as long short-term memory (LSTM), and its later variation has been prevalently adopted for training large language models on large (language) datasets, such as the Wikipedia corpus and Common Crawl.
2024b
- (ChatGPT-OpenAI, 2024) ⇒ Prompt: Summarize the concept Decoder-Only Transformer-based Neural Language Model in 1- 2 paragraphs. ChatGPT (model:gpt-4-turbo-preview). Retrieved: 2024-02-18.
- QUOTE: A Decoder-Only Transformer-based Neural Language Model represents a specific architecture in the domain of natural language processing and machine learning. Unlike the more prevalent encoder-decoder architectures, where the encoder processes the input data and the decoder generates output based on the encoded information, a decoder-only model focuses solely on the decoding part of this arrangement. Utilizing the Transformer architecture—known for its self-attention mechanism that allows the model to weigh the importance of different parts of the input data when producing an output—this approach is streamlined for tasks involving text generation.
Decoder-only models, such as GPT, are pre-trained on vast amounts of text data, enabling them to understand and generate human-like text. During training, the model learns to predict the next word in a sentence given the words that come before it, effectively teaching it the probabilities of word sequences. This form of unsupervised learning is beneficial for a wide range of applications, including but not limited to, language generation, summarization, translation, and even answering questions. The strength of this architecture lies in its simplicity and efficiency in generating coherent and contextually relevant text, making it a powerful tool in the field of AI-driven text generation and processing.
- QUOTE: A Decoder-Only Transformer-based Neural Language Model represents a specific architecture in the domain of natural language processing and machine learning. Unlike the more prevalent encoder-decoder architectures, where the encoder processes the input data and the decoder generates output based on the encoded information, a decoder-only model focuses solely on the decoding part of this arrangement. Utilizing the Transformer architecture—known for its self-attention mechanism that allows the model to weigh the importance of different parts of the input data when producing an output—this approach is streamlined for tasks involving text generation.