Here is a comprehensive table that compares some of the most popular AI language models on key comparative dimensions.
Now, let's turn to the topic of hyperparameters and their influence on model performance.
Hyperparameters define high-level characteristics of a model's learning process, including learning rate, batch size, number of layers, and more. They are not directly learned through the model training process but are set prior to training.
In models like GPT-3, the scale of the model in terms of the number of transformer layers, hidden units, and attention heads play a crucial role.
It has been observed that larger models generally perform better, provided the availability of sufficient quality data and computational resources.
The learning rate is a critical hyperparameter in the training process as well. A well-tuned learning rate ensures that the model converges to a good solution.
BERT and its variant, RoBERTa, also require the tuning of several hyperparameters.
These include learning rate, batch size, number of training steps, and the number of warm-up steps. RoBERTa, in particular, uses a larger batch size and byte-level BPE, which results in overall improved performance compared to BERT.
For Transformer-XL, segment size is a crucial hyperparameter.
Longer segment lengths can effectively model longer-term dependency, which is particularly important in tasks that require understanding context over large spans of text.
Each of these models excels in different applications due to their unique characteristics.
GPT-3 is particularly powerful in generating human-like text and thus is excellent for tasks like writing essays, fictional stories, or generating code.
BERT and RoBERTa are potent tools for understanding the context (semantic meaning) of words in a sentence and thus excel in tasks like sentiment analysis, named entity recognition, or question answering.
Transformer-XL, with its ability to handle longer contexts (larger number of tokens in a context window), is great for tasks that require the model to remember distant information, such as summarizing long documents, generating detailed narratives, or doing reasoning or answering questions on larger bodies of text.
Choosing the right model and tuning its hyperparameters for a particular task is more art than science and requires a good understanding of both the task at hand, the tools available, and the business domain within which you are asking the questions.
3 key factors in choosing the Model:
Resources / money to buy cloud computing time. Tools available. Skillsets in the development team. Business needs and business domain that you want the Language Model to provide solutions for. Example, if I were writing a Chat box to answer customer questions on a website, that would be a different requirement structure that writing a language model to hold token context windows for dozens of books and provide concept classifications that spanned all of those books.
Remember, there's no one-size-fits-all solution in AI; it's all about finding the best tool for the job.
The hyperparameters for each language model vary depending on the specific implementation and use case.
For example, the number of layers, hidden size, and sequence length are common hyperparameters that can be adjusted to optimize the performance of a language model.
In general, increasing the number of layers and hidden size can improve the accuracy of the model, but also increase the computational cost and training time. The sequence length determines the maximum length of input text that the model can handle.
When it comes to choosing the best AI language model for a specific application, it depends on the specific requirements and constraints of the task.
For example, GPT-3 is known for its impressive language generation capabilities, making it a good choice for chatbots and Q&A systems.
BERT and RoBERTa are popular choices for language understanding tasks such as sentiment analysis and text classification.
ULMFiT is a good choice for tasks that require fine-tuning on a specific domain or dataset. It is important to consider factors such as the size of the pre-training data, the computational resources required for training and inference, and the specific requirements of the task when selecting an AI language model.
Inference: to make a guess based on available data. If the language model is asked to connect tokens which it does not have “close proximity” token windows to answer, it will reach out to more distance context windows and find the highest weighted connections it can present, to generate an answer from.