Predicting labels with LLMs for tabular data

We will explore the process of predicting labels for tabular data classification tasks using large language models (LLMs) and few-shot learning.

In the case of few-shot learning, we provide a few training data examples in the form of text along with a prompt for the model. The model adapts to the context and responds to new questions from the user.

First, let’s examine how to predict labels using LLMs for tabular data.

For tabular data, the initial step involves converting the data into serialized text data using LangChain’s templates. LangChain templates allow converting rows of data into fluent sentences or paragraphs by mapping columns to text snippets with variables that are filled based on cell values. Once we have the text data, we can utilize it as few-shot examples, comprising pairs of questions along with their corresponding labels (answers). Subsequently, we will send this few-shot data to the model.

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