Bootstrap

Bootstrapping a new language model from running one by feedings its outputs back into itself as input.

Bootstrapping a new language model from running one involves the process of training a new model using the outputs of an existing model as input. This technique is known as "self-training" or "bootstrapping". Here are the general steps to bootstrap a new language model:

1. Collect and preprocess a large corpus of text data in the target language(s).
2. Train a preliminary language model on the collected data.
3. Use the preliminary language model as an initial seed for the bootstrapping process. This can be done by generating a set of new sentences or paragraphs, which are then fed back into the model to generate more output.
4. Repeat the generation and feeding process multiple times, gradually improving the quality of the output and refining the language model as it learns from its own output.
5. Once the bootstrapping process is complete, the resulting language model can be fine-tuned on additional data to further improve its performance.

Bootstrapping a new language model can be a time-consuming and computationally intensive process, but it has been shown to be highly effective in creating high-quality language models that perform well on a wide range of NLP tasks.

Here's a more detailed breakdown of each step in the bootstrapping process:

1. Collect and preprocess a large corpus of text data in the target language(s): The first step in bootstrapping a new language model is to collect a large amount of text data in the target language(s). This data should be cleaned, tokenized, and preprocessed to remove any noise or irrelevant information.
2. Train a preliminary language model on the collected data: Once the data has been preprocessed, it can be used to train a preliminary language model. This can be done using a variety of techniques, including supervised learning or unsupervised learning. The goal of this step is to create a basic model that can generate coherent text in the target language(s).
3. Use the preliminary language model as an initial seed for the bootstrapping process: Once the preliminary language model has been trained, it can be used as an initial seed for the bootstrapping process. This can be done by generating a set of new sentences or paragraphs, which are then fed back into the model to generate more output.
4. Repeat the generation and feeding process multiple times: The bootstrapping process involves repeatedly generating new text and feeding it back into the model. This is known as "self-training". As the model continues to learn from its own output, it will gradually improve in quality. This process should be repeated multiple times until the desired level of accuracy has been achieved.
5. Fine-tune the resulting language model on additional data: Once the bootstrapping process is complete, the resulting language model can be fine-tuned on additional data to further improve its performance. This can be done by training the model on a separate dataset that was not used during the bootstrapping process. By doing this, the model will learn from new data and be able to generate even more coherent text in the target language(s).