“The GPT-3 model, which applies the theory of conditional probability to large-scale language models to predict the next word, was trained and applied to create GPT-3.5, which has shown remarkable performance. However, when you look closely, the learning algorithm itself does not differ significantly from the learning algorithm based on the design principles of Google’s Transformer. Rather, the reason GPT-3.5 has shown such remarkable performance is due to the quantity and quality of the training data used when applying the theory of conditional probability to large-scale language models to predict the next word. I believe that this is the reason why GPT-3.5 has shown such remarkable performance… Do you think my opinion is wrong?”
- Learning Algorithm: GPT-3.5 is based on Google’s Transformer model. The Transformer model uses an “Attention” mechanism that allows each element of the input data to interact with all other elements. This is very important for understanding context, as the meaning of a word in a sentence can greatly vary depending on the context it is placed in. The Transformer model effectively captures this contextual information for understanding and generating language.
- Quantity and Quality of Data: GPT-3.5 uses a large amount of text data collected from the internet for training. This data includes a variety of topics and styles, allowing the model to enhance its ability to understand and generate language in various contexts. Also, the quality of the data is important. The better the training data is cleaned, diversified, and representative, the better the performance of the model. For example, if the model learns from biased data, it can generate biased results. Therefore, using high-quality data is important.
- Conditional Probability: GPT-3.5 uses “conditional probability” to predict the next word. That is, it calculates the probability of what the next word will be given the context. This is used when the model generates sentences, where at each step, the word with the highest probability is selected to complete the sentence.
These factors combine to allow GPT-3.5 to excel in various language tasks. The reason all of this is possible is because GPT-3.5 learns from a large amount of data, through which it understands the complexity and diversity of language. This plays a crucial role in enhancing the model’s ability to understand and generate language in various contexts.
Deep Network, a one-person startup specializing in consulting for super-large language models
E-mail : sayhi7@daum.net
Representative of a one-person startup / SeokWeon Jang