6 citations · 8 across the 7 of their papers we have counts for
9 papers
Can We Trust the Judges? Validation of Factuality Evaluation Methods via Answer Perturbation
Sarra Gharsallah, Adele Robaldo, Mariia Tokareva +5
Evaluating the factual correctness of large language models (LLMs) is vital for many applications. But are our evaluation tools themselves trustworthy? Despite the rise of factuali…
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…
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…
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…
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…
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…
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…
Latent Abstractions in Generative Diffusion Models
Giulio Franzese, Mattia Martini, Giulio Corallo +2
In this work we study how diffusion-based generative models produce high-dimensional data, such as an image, by implicitly relying on a manifestation of a low-dimensional set of la…
Finch: Prompt-guided Key-Value Cache Compression
Giulio Corallo, Paolo Papotti
Recent large language model applications, such as Retrieval-Augmented Generation and chatbots, have led to an increased need to process longer input contexts. However, this require…