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Language models are algorithms that can predict the next word in a sequence of words, based on the words that have come before it.
They are interesting because they can be used in a variety of natural languages processing tasks, such as machine translation, speech recognition, and text generation.
Language models are typically trained on large amounts of text data, which allows them to capture the statistical patterns and relationships between words in a language. This allows them to make predictions about the next word in a sequence that is often very accurate.
What kinds of predictions can language models make?
Language models can make predictions about the next word in a sequence of words, based on the words that have come before it.
They can also be used to generate text that is similar to a given input, by predicting the next word in a sequence and then using that prediction as the input for the next prediction, and so on.
This can be used to generate text that is similar to a given input or to complete sentences or paragraphs that are missing some words.
Language models can also be used in other natural language processing tasks, such as machine translation, speech recognition, and text summarization.
What’s the best way to measure the performance of a language model?
One way to measure the performance of a language model is to evaluate its ability to predict the next word in a sequence of words, based on the words that have come before it. This can be done by using a test set of text data that the model has not seen during training, and comparing the model’s predictions to the actual next word in the sequence. The accuracy of the model’s predictions can then be used as a measure of its performance. Other metrics, such as the perplexity of the model, can also be used to evaluate its performance. Perplexity is a measure of how well a language model predicts a given test set of text data, and is calculated as the exponentiated average of the model’s prediction errors on the test set. A lower perplexity score indicates a better-performing language model.
What does it mean to fine-tune a language model?
Fine-tuning a language model means adjusting its parameters to improve its performance on a specific task or dataset. This is typically done by training the language model on a large amount of text data that is relevant to the task or dataset, in addition to the training data that the model was originally trained on. This allows the model to learn the statistical patterns and relationships between words that are specific to the task or dataset and can improve its performance on that task or dataset. Fine-tuning can be a useful technique for adapting a pre-trained language model to a new task or dataset.
A SERIES OF ARTICLES ABOUT ‘AI, CLOUD, AND ADVANCED TECHNOLOGIES IN EDUCATION’ WRITTEN BY THE IBL AI ENGINE IN DECEMBER 2022*
*The IBL AI/ML Engine extends and hosts leading language models (LLMs) via a combination of fine-tuning, customized datasets and REST APIs to provide an all-in-one AI platform for education featuring content recommendations, assessment creation and grading, chatbots and mentors, and predictive analytics.