Papers by Massimo Rizzoli
CIVET: Systematic Evaluation of Understanding in VLMs (2025.findings-emnlp)
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Massimo Rizzoli, Simone Alghisi, Olha Khomyn, Gabriel Roccabruna, Seyed Mahed Mousavi, Giuseppe Riccardi
| Challenge: | Current Vision-Language Models can accurately recognize only a limited set of basic object properties; 3) they struggle to understand basic relations among objects. |
| Approach: | They propose a framework that evaluates VLMs on exhaustive sets of stimuli, free from annotation noise, dataset-specific biases, and uncontrolled scene complexity. |
| Outcome: | The proposed framework addresses the lack of standardized systematic evaluation for assessing VLMs’ understanding, enabling researchers to test hypotheses with statistical rigor. |
Will LLMs Replace the Encoder-Only Models in Temporal Relation Classification? (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have shown promising performance in temporal reasoning tasks such as temporal question answering. |
| Approach: | They propose to use large language models to detect temporal relations between events with in-context learning and lightweight fine-tuning approaches to assess their performance. |
| Outcome: | The proposed models significantly underperform smaller encoder-only models based on RoBERTa in the Temporal Relation Classification task. |