42 citations · 77 across the 19 of their papers we have counts for
5 papers · 1 filter
The Silent Failures in Agentic Data Science
Xue Li, Hongqian Xia, Daniel Gomm +3
Large language models have significantly lowered the barrier to writing code. Naturally, this has also led to the development of data science agents that promise automating the des…
Open Tabular Insight Extraction: Where Do We Stand, and Where Should We Go?
Daniel Gomm, Maarten de Rijke, Madelon Hulsebos
Democratizing access to the knowledge held in large corpora of tables such as data lakes is emerging as a central research challenge. Research in this space is advancing and broade…
Advancing Open and Reproducible Relational Learning: RelArena-, TabPFN-Rel and RPI
Adrian Hayler, Klemens Flöge, Alan Arazi +44
This first release of Prior Labs in relational learning shows our continued commitment to open science. We open-source three pieces of software that we expect to accelerate researc…
Fine-Grained Table Retrieval Through the Lens of Complex Queries
Wojciech Kosiuk, Xingyu Ji, Yeounoh Chung +2
Enabling question answering over tables and databases in natural language has become a key capability in the democratization of insights from tabular data sources. These systems fi…
SQaLe: A Large Text-to-SQL Corpus Grounded in Real Schemas
Cornelius Wolff, Daniel Gomm, Madelon Hulsebos
Advances in large language models have accelerated progress in text-to-SQL, methods for converting natural language queries into valid SQL queries. A key bottleneck for developing…