4 citations · 8 across the 12 of their papers we have counts for
13 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…
MATEO: A Multimodal Benchmark for Temporal Reasoning and Planning in LVLMs
Gabriel Roccabruna, Olha Khomyn, Giuseppe Riccardi
AI agents need to plan to achieve complex goals that involve orchestrating perception, sub-goal decomposition, and execution. These plans consist of ordered steps structured accord…
What Does Loss Optimization Actually Teach, If Anything? Knowledge Dynamics in Continual Pre-training of LLMs
Seyed Mahed Mousavi, Simone Alghisi, Giuseppe Riccardi
Continual Pre-Training (CPT) is widely used for acquiring and updating factual knowledge in LLMs. This practice treats loss as a proxy for knowledge learning, while offering no gro…
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…
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…
DyKnow: Dynamically Verifying Time-Sensitive Factual Knowledge in LLMs
Seyed Mahed Mousavi, Simone Alghisi, Giuseppe Riccardi
LLMs acquire knowledge from massive data snapshots collected at different timestamps. Their knowledge is then commonly evaluated using static benchmarks. However, factual knowledge…
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…