Generative AI Fundamentals: Examples of Pre-Training for Large Language Models

Learn about the examples of pre-training for large language models, such as document summarization, text classification, and question answering. Find out how different methods of pre-training can handle these tasks.

Question

Which of the following are examples of pre-training for a large language model (LLM)? (Select 3)

A. Document summarization
B. Text classification
C. Question answering
D. Financial forecasting

Answer

A. Document summarization
B. Text classification
C. Question answering

Explanation

The correct answers are A, B, and C.

Pre-training for a large language model (LLM) is the process of training the model on a large corpus of unlabeled text data to learn general linguistic knowledge and capabilities. Pre-training can be done using different methods, such as masked language models, auto-regressive language models, or text-to-text models. These methods can handle various tasks, such as document summarization, text classification, and question answering, by generating text, predicting missing words, or converting one sequence of text into another.

Document summarization is an example of pre-training for a LLM, as it involves generating a concise summary of a longer document. Text-to-text models, such as T5, can be pre-trained on document summarization tasks using large datasets, such as C4.

Text classification is another example of pre-training for a LLM, as it involves assigning a label or category to a text. Masked language models, such as BERT, can be pre-trained on text classification tasks using large datasets, such as GLUE.

Question answering is a third example of pre-training for a LLM, as it involves generating an answer to a natural language question. Auto-regressive language models, such as GPT, can be pre-trained on question answering tasks using large datasets, such as SQuAD.

Financial forecasting is not an example of pre-training for a LLM, as it involves predicting future values of financial variables, such as stock prices or exchange rates. This task does not require generating or processing natural language, and it is not suitable for pre-training LLMs.

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