Here are five ways LLMs are used in machine learning for data science:
Topic modeling is an unstructured machine learning technique that detects clusters of related words and phrases within unstructured text, such as emails, customer service responses, and social media posts. Using topic modeling, data scientists can help organizations identify relevant themes to improve processes. For example, an analysis of customer complaints may reveal themes that indicate a quality control issue with a certain product or shortcomings in customer support processes.
Text classification is a structured ML practice that uses text classifiers to label documents based on their content. Large language models assist in automating the categorization of text documents into organized groups. Text classification is integral to numerous ML-powered processes, including sentiment analysis, document analysis, spam detection, and language translation.
Data cleansing and imputation
Preparing data for analysis can be tedious and time-consuming. Large language models can automate many data cleansing tasks, including flagging duplicate data, data parsing and standardization, and identifying anomalies or outliers.
Large language models can be useful in data annotation and labeling tasks. They can propose labels or tags for text data, reducing the manual effort required for annotation. This assistance speeds up the labeling process and allows data scientists to focus on more complex tasks.
Automating data science workflows
Large language models can be used to automate a variety of data science tasks. One example is text summarization. With their ability to quickly analyze and summarize large volumes of textual data, large language models can generate concise summaries of long texts such as podcast transcripts. These summaries can then be analyzed to quickly identify key points and observe patterns and trends. By automating time-consuming processes, large language models free data scientists to focus on deeper analysis and improved decision-making.