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Generating data for language model training through prompting
Language models (LLMs) such as GPT-3 play a large role not only in creating consistent text, but also in generating data for various purposes. For example, LLM can be used to create specific patterns of data for sentiment analysis.
Example of data generation for sentiment analysis
Here's how to use LLM to generate data for sentiment analysis:
Example creation process: Generate 10 examples, including positive and negative phrases.
Example output: The phrase “I just heard the best news!” is labeled with the sentiment word ‘positive’, while the phrase “It’s so gloomy outside” is labeled with the sentiment word ‘negative’.
In Korean, there are emotion classification datasets called nsmc and sarcasm. If the dataset was created one by one, using a language model can create thousands or tens of thousands of datasets at once.
The usefulness and flexibility of an LLM
Creating and supplying your own data sets like this has a huge impact on LLM. LLM is useful for quickly generating data for experimentation, testing, and training purposes. It can adapt data in a variety of formats and styles to fit your needs, which is especially important in fields that require large and diverse data sets, such as machine learning.
Use cases for generated data
The data generated can be utilized in the following ways:
Train a machine learning model: You can use the generated data to train a sentiment analysis model.
Benchmarking and testing: Evaluate the performance of existing models on new data.
Research and Analysis: Conduct research or research on sentiment analysis.
In the past, building and operating a complete dataset was very difficult. With the advent of the language model era, it would be good to know that creating data and securing training data has become easier. To put it simply, students raise their grades by creating and solving problems on their own. You can understand it to that extent. These capabilities open up many possibilities for researchers, data scientists, and developers, making LLM an important tool in the AI toolkit.
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