activity
20222025
most citedFENICE: Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction

2 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2025★ 1 cited

Right Answer, Wrong Score: Uncovering the Inconsistencies of LLM Evaluation in Multiple-Choice Question Answering

Francesco Maria Molfese, Luca Moroni, Luca Gioffré +3

One of the most widely used tasks for evaluating Large Language Models (LLMs) is Multiple-Choice Question Answering (MCQA). While open-ended question answering tasks are more chall…

cs.CL2024

Truth or Mirage? Towards End-to-End Factuality Evaluation with LLM-Oasis

Alessandro Scirè, Andrei Stefan Bejgu, Simone Tedeschi +3

After the introduction of Large Language Models (LLMs), there have been substantial improvements in the performance of Natural Language Generation (NLG) tasks, including Text Summa…

cs.CL2024

Guardians of the Machine Translation Meta-Evaluation: Sentinel Metrics Fall In!

Stefano Perrella, Lorenzo Proietti, Alessandro Scirè +2

Annually, at the Conference of Machine Translation (WMT), the Metrics Shared Task organizers conduct the meta-evaluation of Machine Translation (MT) metrics, ranking them according…

cs.CL2024★ 2 cited

FENICE: Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction

Alessandro Scirè, Karim Ghonim, Roberto Navigli

Recent advancements in text summarization, particularly with the advent of Large Language Models (LLMs), have shown remarkable performance. However, a notable challenge persists as…

cs.CL2023

Echoes from Alexandria: A Large Resource for Multilingual Book Summarization

Alessandro Scirè, Simone Conia, Simone Ciciliano +1

In recent years, research in text summarization has mainly focused on the news domain, where texts are typically short and have strong layout features. The task of full-book summar…

cs.CL2022

Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures

Simone Conia, Edoardo Barba, Alessandro Scirè +1

One of the common traits of past and present approaches for Semantic Role Labeling (SRL) is that they rely upon discrete labels drawn from a predefined linguistic inventory to clas…