One of the most mundane data science tasks that can be automated with a large language model (LLM) is data cleaning. LLMs can be trained to recognize patterns in data and automatically correct errors or inconsistencies. This can save data scientists and analysts a significant amount of time and effort.
Another task that can be automated with LLMs is data tagging. Many machine learning projects require large amounts of labeled data, which can be time-consuming and tedious to produce manually. LLMs can be used to automatically tag data based on predefined rules or patterns, reducing the need for manual labeling.
Additionally, LLMs can be used to generate reports or summaries of large datasets. This can save analysts a significant amount of time and effort, as they no longer need to manually sift through large amounts of data to identify important trends or insights.
Finally, LLMs can be used to automate the process of model building and selection. By training an LLM on large amounts of data, it can learn to identify the most effective models and hyperparameters for a given problem. This can save data scientists and analysts a significant amount of time and effort, as they no longer need to manually test and select models for each problem they encounter.
