activity
20242026
most citedThink2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL

1 citations · 1 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CL2026

Automated Alignment between Elicitation Interviews and Requirements

Francesco Dente, Fabiano Dalpiaz, Paolo Papotti

Software requirements are derived from a variety of elicitation techniques, many of which have a conversational nature, like interviews. However, evaluating whether those derived r…

cs.LG20261 cited

Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL

Simone Papicchio, Simone Rossi, Luca Cagliero +1

Large Language Models (LLMs) can translate natural language into SQL, but small models struggle with multi-table and complex queries in Zero-Shot Learning (ZSL) settings. While Sup…

cs.CV2026

MedScribe: Clinically Grounded CT Reporting through Agentic Workflows

Giuseppe A. Orlando, Paolo Papotti, Maria A. Zuluaga +2

Vision-language models (VLMs) have shown potential for automated radiology report generation, yet existing approaches rely on global embedding compression of volumetric data, often…

cs.AI2026

Parallel Context-of-Experts Decoding for Retrieval Augmented Generation

Giulio Corallo, Paolo Papotti

Retrieval Augmented Generation faces a trade-off: concatenating documents in a long prompt enables multi-document reasoning but creates prefill bottlenecks, while encoding document…

cs.CL2025

Combating Misinformation in the Arab World: Challenges & Opportunities

Azza Abouzied, Firoj Alam, Raian Ali +1

Misinformation and disinformation pose significant risks globally, with the Arab region facing unique vulnerabilities due to geopolitical instabilities, linguistic diversity, and c…

cs.CL2025

Beyond RAG: Task-Aware KV Cache Compression for Comprehensive Knowledge Reasoning

Giulio Corallo, Orion Weller, Fabio Petroni +1

Incorporating external knowledge in large language models (LLMs) enhances their utility across diverse applications, but existing methods have trade-offs. Retrieval-Augmented Gener…