Table Serializer Kitchen

A flexible, extensible, and easy-to-use package for serializing tabular data for the use with LLMs.

The Table Serialization Kitchen is a project based on my work on benchmarking table retrieval through dense retrieval (Gomm & Hulsebos, 2025).

Use the Table Serialization Kitchen to boil your tabular data down to serializations. Provide a recipe and some ingredients and serialize your tables in no-time. You can easily spice things up by extending Table Serialization Kitchen with your own serialization ideas!

References

2025

Metadata Matters in Dense Table Retrieval

Daniel Gomm, and Madelon Hulsebos

In ELLIS workshop on Representation Learning and Generative Models for Structured Data, 2025

Abs Bib PDF Blog

Recent advances in Large Language Models have enabled powerful systems that perform tasks by reasoning over tabular data. While these systems typically assume relevant data is provided with a query, real-world use cases are mostly open-domain, meaning they receive a query without context regarding the underlying tables. Retrieving relevant tables is typically done over dense embeddings of serialized tables. Yet, there is a limited understanding of the effectiveness of different inputs and serialization methods for using such off-the-shelf text-embedding models for table retrieval. In this work, we show that different serialization strategies result in significant variations in retrieval performance. Additionally, we surface shortcomings in commonly used benchmarks applied in open-domain settings, motivating further study and refinement.
@inproceedings{gomm2025metadata, title = {Metadata Matters in Dense Table Retrieval}, author = {Gomm, Daniel and Hulsebos, Madelon}, booktitle = {ELLIS workshop on Representation Learning and Generative Models for Structured Data}, year = {2025}, url = {https://openreview.net/forum?id=rELWIvq2Qy}, }