4 citations · 6 across the 9 of their papers we have counts for
9 papers
V-DyKnow: A Dynamic Benchmark for Time-Sensitive Knowledge in Vision Language Models
Seyed Mahed Mousavi, Christian Moiola, Massimo Rizzoli +2
Vision-Language Models (VLMs) are trained on data snapshots of documents, including images and texts. Their training data and evaluation benchmarks are typically static, implicitly…
Getting to the Point: Pointing Improves LVLMs at Counting
Simone Alghisi, Massimo Rizzoli, Seyed Mahed Mousavi +1
Pointing-based methods decompose complex tasks as sequential grounding and reasoning steps. Given a query, the model first grounds the relevant objects by generating their coordina…
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…
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…
Will LLMs Replace the Encoder-Only Models in Temporal Relation Classification?
Gabriel Roccabruna, Massimo Rizzoli, Giuseppe Riccardi
The automatic detection of temporal relations among events has been mainly investigated with encoder-only models such as RoBERTa. Large Language Models (LLM) have recently shown pr…
Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for Dialogue
Simone Alghisi, Massimo Rizzoli, Gabriel Roccabruna +2
We study the limitations of Large Language Models (LLMs) for the task of response generation in human-machine dialogue. Several techniques have been proposed in the literature for…
Are LLMs Robust for Spoken Dialogues?
Seyed Mahed Mousavi, Gabriel Roccabruna, Simone Alghisi +3
Large Pre-Trained Language Models have demonstrated state-of-the-art performance in different downstream tasks, including dialogue state tracking and end-to-end response generation…
Reconstructing Bayesian Networks on a Quantum Annealer
Enrico Zardini, Massimo Rizzoli, Sebastiano Dissegna +2
Bayesian networks are widely used probabilistic graphical models, whose structure is hard to learn starting from the generated data. O'Gorman et al. have proposed an algorithm to e…