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If you’re curious about the technology behind ChatGPT, this article will provide you with a brief introduction to the machine learning models that power the chatbot. We’ll start by introducing Large Language Models (LLMs), then move on to the self-attention mechanism behind GPT-3, and finish with Reinforcement Learning From Human Feedback.
TLDR
- ChatGPT is a chatbot that can understand and respond to people’s messages.
- ChatGPT is powered by advanced machine learning technology.
- Large Language Models (LLMs) are the type of machine learning models that ChatGPT uses to understand language.
- LLMs process a lot of text to learn how words are related to each other.
- The Google Brain team created transformers in 2017 to make language models better.
- Transformers can process all the words in a message at the same time and understand the importance of each word.
- GPT models use a self-attention mechanism to understand the meaning and context of a message.
- ChatGPT uses Reinforcement Learning From Human Feedback (RLHF) to improve the accuracy of its responses.
- RLHF allows ChatGPT to learn from feedback from people and understand their intentions better.
- ChatGPT’s technology makes it a smart chatbot that can understand and respond to people’s messages accurately.
LLMs are a class of machine learning models that process massive quantities of text data to infer relationships between words. With advancements in computational power, LLMs have grown in capability as the size of input datasets and parameter space increases. While most language models involve predicting a word in a sequence of words, the basic sequencing technique has two significant limitations. First, it doesn’t value some of the surrounding words more than others. Second, it processes input data individually and sequentially, rather than as a whole corpus.
In response to these limitations, the Google Brain team introduced transformers in 2017. Unlike the basic sequencing technique, transformers can process all input data simultaneously, giving varying weight to different parts of the input data to infer meaning and context. The transformer architecture led to the creation of Generative Pre-training Transformer (GPT) models in 2018, which have since evolved into more advanced versions like GPT-3, InstructGPT, and ChatGPT.
GPT models use a multi-head self-attention mechanism to infer meaning and context from input sequences. This mechanism converts tokens into vectors that represent their importance within the sequence.
The self-attention mechanism is at the core of GPT’s ability to understand and contextualize language. It works by transforming tokens, which can be a word, sentence, or other grouping of text, into vectors that reflect their significance in the input sequence. To accomplish this, the model:
- Creates a query, key, and value vector for each token in the input sequence.
- Measures the similarity between the query vector generated in step one and the key vector of every other token by computing the dot product of the two vectors.
- Normalizes the similarity scores obtained in step 2 using a softmax function to generate weights.
- Computes a final vector that encapsulates the significance of the token within the sequence by multiplying the weights produced in step 3 by the value vectors of each token.
By iterating the self-attention mechanism several times, GPT models can grasp sub-meanings and complex relationships within the input data. However, GPT-3 is limited in its ability to align with user intentions and may produce outputs that lack helpfulness, lack interpretability, or include un-intended content.
To counteract these issues, innovative training methodologies were introduced in ChatGPT, which is a spinoff of InstructGPT. Reinforcement Learning From Human Feedback (RLHF) is the novel approach used to incorporate human feedback into the training process and align the model outputs with user intent.
Overall, ChatGPT uses LLMs, transformers, and GPT models to provide a chatbot with the ability to understand and respond to users’ intents. With the integration of RLHF, ChatGPT has become a more advanced and accurate chatbot.
Sources:
Arxiv: https://arxiv.org/pdf/2203.02155.pdf
DeepAI: https://deepai.org/machine-learning-glossary-and-terms/softmax-layer
OpenAI: https://openai.com/blog/chatgpt/
AssemblyAI: https://www.assemblyai.com/blog/how-chatgpt-actually-works/
Towards Data Science: https://towardsdatascience.com/proximal-policy-optimization-ppo-explained-abed1952457b
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