48 citations · 119 across the 16 of their papers we have counts for
17 papers
Test-Time Verification for Text-to-SQL via Outcome Reward Models
Mattia Tritto, Giuseppe Farano, Dario Di Palma +4
Improving the reliability of large language models (LLMs) at inference time is a central challenge in structured reasoning tasks such as Text-to-SQL. Common test-time inference str…
Exploring Approaches for Detecting Memorization of Recommender System Data in Large Language Models
Antonio Colacicco, Vito Guida, Dario Di Palma +2
Large Language Models (LLMs) are increasingly applied in recommendation scenarios due to their strong natural language understanding and generation capabilities. However, they are…
Exploring Diversity, Novelty, and Popularity Bias in ChatGPT's Recommendations
Dario Di Palma, Giovanni Maria Biancofiore, Vito Walter Anelli +2
ChatGPT has emerged as a versatile tool, demonstrating capabilities across diverse domains. Given these successes, the Recommender Systems (RSs) community has begun investigating i…
LLMs for Automated Unit Test Generation and Assessment in Java: The AgoneTest Framework
Andrea Lops, Fedelucio Narducci, Azzurra Ragone +2
Unit testing is an essential but resource-intensive step in software development, ensuring individual code units function correctly. This paper introduces AgoneTest, an automated e…
GradeSQL: Test-Time Inference with Outcome Reward Models for Text-to-SQL Generation from Large Language Models
Mattia Tritto, Giuseppe Farano, Dario Di Palma +4
Text-to-SQL, the task of translating natural language questions into SQL queries, has significantly advanced with the introduction of Large Language Models (LLMs), broadening datab…
Do Recommender Systems Really Leverage Multimodal Content? A Comprehensive Analysis on Multimodal Representations for Recommendation
Claudio Pomo, Matteo Attimonelli, Danilo Danese +2
Multimodal Recommender Systems aim to improve recommendation accuracy by integrating heterogeneous content, such as images and textual metadata. While effective, it remains unclear…
LLaMAs Have Feelings Too: Unveiling Sentiment and Emotion Representations in LLaMA Models Through Probing
Dario Di Palma, Alessandro De Bellis, Giovanni Servedio +3
Large Language Models (LLMs) have rapidly become central to NLP, demonstrating their ability to adapt to various tasks through prompting techniques, including sentiment analysis. H…
Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M
Dario Di Palma, Felice Antonio Merra, Maurizio Sfilio +3
Large Language Models (LLMs) have become increasingly central to recommendation scenarios due to their remarkable natural language understanding and generation capabilities. Althou…
A System for Automated Unit Test Generation Using Large Language Models and Assessment of Generated Test Suites
Andrea Lops, Fedelucio Narducci, Azzurra Ragone +2
Unit tests represent the most basic level of testing within the software testing lifecycle and are crucial to ensuring software correctness. Designing and creating unit tests is a…
Evaluating ChatGPT as a Recommender System: A Rigorous Approach
Dario Di Palma, Giovanni Maria Biancofiore, Vito Walter Anelli +3
Large Language Models (LLMs) have recently shown impressive abilities in handling various natural language-related tasks. Among different LLMs, current studies have assessed ChatGP…
Counterfactual Fair Opportunity: Measuring Decision Model Fairness with Counterfactual Reasoning
Giandomenico Cornacchia, Vito Walter Anelli, Fedelucio Narducci +2
The increasing application of Artificial Intelligence and Machine Learning models poses potential risks of unfair behavior and, in light of recent regulations, has attracted the at…
Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness setting
Giandomenico Cornacchia, Vito Walter Anelli, Fedelucio Narducci +2
Current AI regulations require discarding sensitive features (e.g., gender, race, religion) in the algorithm's decision-making process to prevent unfair outcomes. However, even wit…
MAFUS: a Framework to predict mortality risk in MAFLD subjects
Domenico Lofù, Paolo Sorino, Tommaso Colafiglio +4
Metabolic (dysfunction) associated fatty liver disease (MAFLD) establishes new criteria for diagnosing fatty liver disease independent of alcohol consumption and concurrent viral h…
Interactive Question Answering Systems: Literature Review
Giovanni Maria Biancofiore, Yashar Deldjoo, Tommaso Di Noia +2
Question answering systems are recognized as popular and frequently effective means of information seeking on the web. In such systems, information seekers can receive a concise re…
Conversational Recommendation: Theoretical Model and Complexity Analysis
Tommaso Di Noia, Francesco Donini, Dietmar Jannach +2
Recommender systems are software applications that help users find items of interest in situations of information overload in a personalized way, using knowledge about the needs an…
FedeRank: User Controlled Feedback with Federated Recommender Systems
Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia +2
Recommender systems have shown to be a successful representative of how data availability can ease our everyday digital life. However, data privacy is one of the most prominent con…
How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank
Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia +2
Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a va…