Decoder-Only Transformer Architecture
A Decoder-Only Transformer Architecture is a transformer architecture that exclusively utilizes the decoder component of the standard transformer model for tasks like language modeling and text generation.
- Context:
- It can (typically) be designed to generate text by predicting the next word or token in a sequence based on the previous words.
- It can (often) be used in natural language processing applications such as chatbots, text completion, and creative writing aids.
- It can be characterized by its attention mechanism, which allows it to focus on different parts of the input sequence when generating each word in the output.
- It can be trained on large corpora of text data, enabling it to capture a wide range of language patterns and styles.
- It can (often) be fine-tuned for specific tasks or domains to enhance its performance or adapt it to specialized types of text.
- It can vary in the number of layers and the size of the model, affecting its complexity and computational requirements.
- It can (typically) use only the decoder of a Transformer model, focusing on the generation of text by processing each word in a sequence such that the attention layers only have access to preceding words, making these models inherently auto-regressive.
- It can (often) be employed for tasks that involve text generation, as its architecture is particularly suited for predicting the next word in a sentence based on the words that come before it.
- It can (typically) omit the encoder part, making it distinct from encoder-decoder models which are used for tasks like machine translation where both the input and output sequences need to be understood and processed.
- ...
- Example(s):
- Conditional Transformer Language Model (CTRL) by Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong and Richard Socher.
- GPT-3, which uses a decoder-only transformer architecture for tasks like text generation and question answering.
- GPT-4, an advanced version of GPT-3 with more layers and parameters, also based on a decoder-only transformer architecture.
- Transformer XL,
- OpenAI's Codex,
- Some specialized versions of decoder-only transformer architectures that are optimized for specific languages or domains, like legal or medical text generation.
- Counter-Example(s):
- Encoder-Only Transformer Architecture, such as BERT, which is designed primarily for understanding and interpreting text rather than generating it.
- Encoder-Decoder Transformer Architecture, like the original transformer model used in machine translation, which combines both encoding and decoding components; or like T5 and BART, which are used for tasks requiring an understanding of the relationship between input and output sequences.
- See: Transformer Model, Attention Mechanism, Natural Language Processing, Text Generation, Fine-Tuning in Machine Learning, Auto-regressive Model, Transformer Model Architecture, Text Generation, NLP Task.
References
2024a
- (HuggingFace, 2024) ⇒ https://huggingface.co/learn/nlp-course/chapter1/6 Retrieved: 2024-02-18.
- QUOTE: Decoder models use only the decoder of a Transformer model. At each stage, for a given word the attention layers can only access the words positioned before it in the sentence. These models are often called auto-regressive models.
The pretraining of decoder models usually revolves around predicting the next word in the sentence.
These models are best suited for tasks involving text generation.
Representatives of this family of models include:
- QUOTE: Decoder models use only the decoder of a Transformer model. At each stage, for a given word the attention layers can only access the words positioned before it in the sentence. These models are often called auto-regressive models.
2024b
- (ChatGPT-OpenAI, 2024) ⇒ Prompt: Summarize the concept Decoder-Only Transformer Architecture in 1- 2 paragraphs. ChatGPT (model:gpt-4-turbo-preview). Retrieved: 2024-02-18.
- QUOTE:The Decoder-Only Transformer Architecture refers to a specific configuration of the Transformer model that employs only the decoder component, diverging from the traditional encoder-decoder architecture typically used in sequence-to-sequence tasks. Originally, Transformers were designed with both encoder and decoder blocks to handle tasks such as machine translation, where the encoder processes the input sequence and the decoder generates the output sequence based on the encoder's output and its own previous outputs.
In a Decoder-Only setup, the model is tailored for generative tasks and can be used for applications like text generation, language modeling, and even some types of conditional generation tasks without the explicit need for an encoder. This architecture leverages self-attention mechanisms within the decoder to process the input data and generate outputs sequentially. By doing so, the model can effectively capture long-range dependencies and contextual information within the input sequence, making it powerful for generating coherent and contextually relevant text. Given its simplified structure, focusing solely on the decoder part, it also allows for potentially more streamlined training and deployment processes, especially in scenarios where the generation of content is the primary objective rather than understanding or translating input sequences.
- QUOTE:The Decoder-Only Transformer Architecture refers to a specific configuration of the Transformer model that employs only the decoder component, diverging from the traditional encoder-decoder architecture typically used in sequence-to-sequence tasks. Originally, Transformers were designed with both encoder and decoder blocks to handle tasks such as machine translation, where the encoder processes the input sequence and the decoder generates the output sequence based on the encoder's output and its own previous outputs.