In one of the generative AI courses, it is mentioned that GPT group of models are decoder only models which means it can generate text. I thought Q&A, translation and summarization type of task require an encoder-decoder model. So, how is GPT models able to do these task if they are only decoder models
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I’m not an AI scientist, I’m only clever enough to warm up the GPT-4 context on the topic, relevant papers, keep it off the internet, and articulate a prompt for you, which gives:
Title: Understanding the Decoder-Only Architecture of GPT Transformers
Introduction
In the realm of Natural Language Processing (NLP), transformer-based language models have revolutionized the way machines understand and generate human language. The traditional transformer model consists of two main components: an encoder and a decoder. However, OpenAI’s GPT (Generative Pretrained Transformer) models have deviated from this norm by adopting a decoder-only architecture. This article aims to demystify the workings of this decoder-only transformer and explain how it manages to perform tasks typically associated with encoder-decoder models.
Understanding Encoders and Decoders
In a traditional transformer model, the encoder and decoder work together to process language. The encoder’s role is to take in the input data (such as a sentence in English) and transform it into a higher, more abstract representation. This process is known as encoding. The encoded data is a complex representation that captures the semantic and syntactic properties of the input data.
The decoder, on the other hand, takes this encoded data and generates the output (such as a translated sentence in French). The decoder uses a mechanism called attention, which allows it to focus on different parts of the input when generating each part of the output.
The Decoder-Only Architecture of GPT Transformers
GPT models, however, do not use an encoder. Instead, they are with a decoder-only architecture. This means that the input data is fed directly into the decoder without being transformed into a higher, more abstract representation by an encoder.
The decoder in a GPT model uses a specific type of attention mechanism known as masked self-attention. In a traditional transformer, the attention mechanism allows the model to focus on all parts of the input when generating each part of the output. However, in a decoder-only transformer like GPT, the attention mechanism is “masked” to prevent it from looking at future parts of the input when generating each part of the output. This is necessary because GPT models are trained to predict the next word in a sentence, so they should not have access to future words.
Why Decoder-Only?
The decoder-only architecture simplifies the model and makes it more efficient for certain tasks, like language modeling. By removing the encoder, GPT models can process input data more directly and generate output more quickly. This architecture also allows GPT models to be trained on a large amount of unlabeled data, which is a significant advantage in the field of NLP where labeled data is often scarce.
Can GPT Models Perform Tasks Typically Associated with Encoder-Decoder Models?
You might wonder, “I thought Q&A, translation, and summarization tasks require an encoder-decoder model. So, how are GPT models able to do these tasks if they are only decoder models?”
The answer lies in the power of the transformer’s decoder and the training method used for GPT models. Even without an encoder, the decoder in a transformer is capable of understanding the input data and generating appropriate output. This is because the decoder uses self-attention, which allows it to focus on different parts of the input when generating the output.
Moreover, GPT models are trained using a method called unsupervised learning. They are trained to predict the next word in a sentence, given all the previous words. This training method allows GPT models to learn a lot about the structure and semantics of the language, which enables them to perform tasks like translation, summarization, and Q&A.
In conclusion, GPT’s decoder-only architecture is a powerful and efficient alternative to the traditional encoder-decoder model. It simplifies the model, makes it more efficient, and allows it to be trained on a large amount of unlabeled data. Despite not having an encoder, GPT models are still capable of performing tasks typically associated with encoder-decoder models, thanks to the power of the transformer’s decoder and the training method used.
(apologies for dumbed-down GPT-4 not being able to write grammatically-correct sentences, a symptom repeated multiple times in my session.)
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There should be a policy against GPT-generated content here. Such content is poison to the reliability of the site.
The answer is not strictly Q&A by AI - this is me providing significantly more context input to the AI than the length of the answer, along with instructions for the composition pieces desired, already knowing the topic (already answering the same before on Reddit), and re-composing the time-saving writing segments into the desired answer, including directly addressing the question.
Below, instead, is completely AI-generated with the push of a button, and we see doesn’t articulate your input correctly:
The discussion began with user joyasree78 questioning how the GPT group of models can perform tasks like Q&A, translation, and summarization when they are decoder-only models, which she thought would require an encoder-decoder model. User _j responded with a detailed explanation about GPT’s decoder-only architecture. They explained that unlike traditional transformer models, GPT models do not use an encoder. Instead, they use a specific type of attention mechanism called masked self-attention. They highlighted that GPT models can perform tasks typically associated with encoder-decoder models due to the power of the transformer’s decoder and the training method used for GPT models. Despite being decoder-only, GPT, through its self-attention mechanism and unsupervised learning method, can understand the input data and generate appropriate output. User jl3, however, voiced concerns about the reliability of GPT-generated content.
Going back to the core of the question:
Not necessarily, the answer is fine tuning, aka further training with smaller but highly curated datasets of text resembling the completion of these tasks, this works because of the process described by @_j
Fine-tuning can increase the core competencies and skills on particular inputs, but even a completion model with no tuning “works” as a GPT-3 decoder-only model.
A completion template example of mine, base curie (press “submit” if you need to see answering on your dime):
https://platform.openai.com/playground/p/o09MQG2o97n6yTl7HvbbaFW6?model=curie
The OpenAI advancement is in not using tagged pre-prepared training that includes semantics, hyponymy, lemmatization, etc or an encoder to extract such abstractions, but simply pretraining on vast amounts of unlabeled data.
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Very true,
Good example btw, the definition of “works” in this sense is quite broad, but you’re correct, the first part of your prompt is what makes the completion models do q&a:
Here is a conversation between an AI customer service chatbot and a user of our web portal, which provides general information and consulting on implement artificial intelligence, machine learning, and associated programming.
The completion of that would be a conversation, the other tasks, like translation, can be completed in a similar way.