Filling in sparse tables with Gretel’s Tabular LLM

Gretel's Tabular LLM is an innovative solution designed to address sparse data issues commonly encountered in tabular datasets. The model leverages the generative capabilities of large language models (LLMs) to create contextually relevant and high-quality responses, which can be used to fill in missing values in a table while maintaining its structured format and preserving field and row-level correlations for numerical, categorical, and free text data types. The approach is particularly useful for e-commerce product listings where multiple constraints need to be reflected in the synthetic insertions. Prompt engineering techniques can further enhance the results by iterating on producing better outputs. Gretel's Tabular LLM early preview is now available for users interested in testing this example or experimenting with their own applications.