2 citations · 3 across the 10 of their papers we have counts for
10 papers
Execution-First Synthetic Tool-Use Trace Generation for LLM Agents
Hafsa Ouajdi, Francesco Giannuzzo, Alaa Boukhary +3
Agentic software-engineering and industrial systems increasingly operate through executable workflows rather than code genera- tion alone: they search artifacts, invoke tools, insp…
Latent Bridges for Multi-Table Question Answering
Simone Varriale, Tamara Cucumides, Floris Geerts +1
We introduce GRAB, a constructor-encoder-bridge pipeline for table question answering. Our method lifts relational data into an heterogeneous graph, encodes it via message passing,…
Selectivity Estimation for Semantic Filters on Image Data
Matthias Urban, Vu Huy Nguyen, Gabriele Sanmartino +2
Semantic data systems integrate Large Language Models (LLMs) and Vision-Language Models (VLMs) directly into database query execution, enabling expressive queries on multi-modal da…
Constraint Decay: The Fragility of LLM Agents in Backend Code Generation
Francesco Dente, Dario Satriani, Paolo Papotti
Large Language Model (LLM) agents demonstrate strong performance in autonomous code generation under loose specifications. However, production-grade software requires strict adhere…
The Stretto Execution Engine for Semantic Data Systems
Gabriele Sanmartino, Matthias Urban, Paolo Papotti +1
Semantic data systems enable querying over both structured and unstructured data using LLMs, but a central challenge is navigating the performance-accuracy trade-off when selecting…
RelationalFactQA: A Benchmark for Evaluating Tabular Fact Retrieval from Large Language Models
Dario Satriani, Enzo Veltri, Donatello Santoro +1
Factuality in Large Language Models (LLMs) is a persistent challenge. Current benchmarks often assess short factual answers, overlooking the critical ability to generate structured…
Community Moderation and the New Epistemology of Fact Checking on Social Media
Isabelle Augenstein, Michiel Bakker, Tanmoy Chakraborty +13
Social media platforms have traditionally relied on internal moderation teams and partnerships with independent fact-checking organizations to identify and flag misleading content.…
An LLM-Based Approach for Insight Generation in Data Analysis
Alberto Sánchez Pérez, Alaa Boukhary, Paolo Papotti +2
Generating insightful and actionable information from databases is critical in data analysis. This paper introduces a novel approach using Large Language Models (LLMs) to automatic…
Retrieve, Merge, Predict: Augmenting Tables with Data Lakes
Riccardo Cappuzzo, Aimee Coelho, Felix Lefebvre +2
Machine-learning from a disparate set of tables, a data lake, requires assembling features by merging and aggregating tables. Data discovery can extend autoML to data tables by aut…
Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison
Kensuke Mitsuzawa, Motonobu Kanagawa, Stefano Bortoli +2
We study two-sample variable selection: identifying variables that discriminate between the distributions of two sets of data vectors. Such variables help scientists understand the…