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20222025
most citedBenchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-Judge

6 citations · 15 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2025

Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models

Riccardo Cantini, Nicola Gabriele, Alessio Orsino +1

Reasoning Language Models (RLMs) have gained traction for their ability to perform complex, multi-step reasoning tasks through mechanisms such as Chain-of-Thought (CoT) prompting o…

cs.DC2025★ 2 cited

Navigating the Edge-Cloud Continuum: A State-of-Practice Survey

Loris Belcastro, Fabrizio Marozzo, Alessio Orsino +2

The edge-cloud continuum has emerged as a transformative paradigm that meets the growing demand for low-latency, scalable, end-to-end service delivery by integrating decentralized…

cs.SI2025

Dynamic hashtag recommendation in social media with trend shift detection and adaptation

Riccardo Cantini, Fabrizio Marozzo, Alessio Orsino +2

Hashtag recommendation systems have emerged as a key tool for automatically suggesting relevant hashtags and enhancing content categorization and search. However, existing static m…

cs.CL2025★ 6 cited

Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-Judge

Riccardo Cantini, Alessio Orsino, Massimo Ruggiero +1

The growing integration of Large Language Models (LLMs) into critical societal domains has raised concerns about embedded biases that can perpetuate stereotypes and undermine fairn…

cs.CL2024★ 2 cited

Are Large Language Models Really Bias-Free? Jailbreak Prompts for Assessing Adversarial Robustness to Bias Elicitation

Riccardo Cantini, Giada Cosenza, Alessio Orsino +1

Large Language Models (LLMs) have revolutionized artificial intelligence, demonstrating remarkable computational power and linguistic capabilities. However, these models are inhere…

cs.DC2022★ 5 cited

Block size estimation for data partitioning in HPC applications using machine learning techniques

Riccardo Cantini, Fabrizio Marozzo, Alessio Orsino +5

The extensive use of HPC infrastructures and frameworks for running dataintensive applications has led to a growing interest in data partitioning techniques and strategies. In fact…