4 citations · 13 across the 14 of their papers we have counts for
5 papers · 1 filter
Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation
Massimo Rizzoli, Simone Alghisi, Seyed Mahed Mousavi +1
Performance gains of Vision Language Models (VLMs) obtained by fine-tuning are generally based on ad hoc data collection and annotation of real-world scenes. Despite the improvemen…
[De|Re]constructing VLMs' Reasoning in Counting
Simone Alghisi, Gabriel Roccabruna, Massimo Rizzoli +2
Vision-Language Models (VLMs) have recently gained attention due to their competitive performance on multiple downstream tasks, achieved by following user-input instructions. Howev…
Garbage In, Reasoning Out? Why Benchmark Scores are Unreliable and What to Do About It
Seyed Mahed Mousavi, Edoardo Cecchinato, Lucia Hornikova +1
We conduct a systematic audit of three widely used reasoning benchmarks, SocialIQa, FauxPas-EAI, and ToMi, and uncover pervasive flaws in both benchmark items and evaluation method…
CIVET: Systematic Evaluation of Understanding in VLMs
Massimo Rizzoli, Simone Alghisi, Olha Khomyn +3
While Vision-Language Models (VLMs) have achieved competitive performance in various tasks, their comprehension of the underlying structure and semantics of a scene remains underst…
LLMs as Repositories of Factual Knowledge: Limitations and Solutions
Seyed Mahed Mousavi, Simone Alghisi, Giuseppe Riccardi
LLMs' sources of knowledge are data snapshots containing factual information about entities collected at different timestamps and from different media types (e.g. wikis, social med…