1 citations · 1 across the 4 of their papers we have counts for
5 papers
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…
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…
Are We Asking the Right Questions? On Ambiguity in Natural Language Queries for Tabular Data Analysis
Daniel Gomm, Cornelius Wolff, Madelon Hulsebos
Natural language interfaces to tabular data must handle ambiguities inherent to queries. Instead of treating ambiguity as a deficiency, we reframe it as a feature of cooperative in…
CoDy: Counterfactual Explainers for Dynamic Graphs
Zhan Qu, Daniel Gomm, Michael Färber
Temporal Graph Neural Networks (TGNNs) are widely used to model dynamic systems where relationships and features evolve over time. Although TGNNs demonstrate strong predictive capa…