1 citations · 2 across the 10 of their papers we have counts for
11 papers
Evaluating Cultural Knowledge Editing in Vision-Language Models
Malvina Nikandrou, Georgios Pantazopoulos, Ioannis Konstas +1
Current Vision-Language Models (VLMs) frequently fail to recognize culture-specific concepts, especially from underrepresented regions. Effectively injecting this long-tail knowled…
GPTNT: Benchmarking Real-Time Collaboration Between Multimodal Agents on Keep Talking And Nobody Explodes
Amit Parekh, Sabrina McCallum, Kareem Al-Hasan +3
Multimodal models are increasingly deployed to solve tasks collaboratively with humans or other artificial agents. While existing benchmarks show that they possess the fundamental…
MoRFI: Monotonic Sparse Autoencoder Feature Identification
Dimitris Dimakopoulos, Shay B. Cohen, Ioannis Konstas
Large language models (LLMs) acquire most of their factual knowledge during the pre-training stage, through next token prediction. Subsequent stages of post-training often introduc…
When Chain-of-Thought Fails, the Solution Hides in the Hidden States
Houman Mehrafarin, Amit Parekh, Ioannis Konstas
Whether intermediate reasoning is computationally useful or merely explanatory depends on whether chain-of-thought (CoT) tokens contain task-relevant information. We present a mech…
Retrievit: In-context Retrieval Capabilities of Transformers, State Space Models, and Hybrid Architectures
Georgios Pantazopoulos, Malvina Nikandrou, Ioannis Konstas +1
Transformers excel at in-context retrieval but suffer from quadratic complexity with sequence length, while State Space Models (SSMs) offer efficient linear-time processing but hav…
Reasoning or a Semblance of it? A Diagnostic Study of Transitive Reasoning in LLMs
Houman Mehrafarin, Arash Eshghi, Ioannis Konstas
Evaluating Large Language Models (LLMs) on reasoning benchmarks demonstrates their ability to solve compositional questions. However, little is known of whether these models engage…
CROPE: Evaluating In-Context Adaptation of Vision and Language Models to Culture-Specific Concepts
Malvina Nikandrou, Georgios Pantazopoulos, Nikolas Vitsakis +2
As Vision and Language models (VLMs) are reaching users across the globe, assessing their cultural understanding has become a critical challenge. In this paper, we introduce CROPE,…
Investigating the Role of Instruction Variety and Task Difficulty in Robotic Manipulation Tasks
Amit Parekh, Nikolas Vitsakis, Alessandro Suglia +1
Evaluating the generalisation capabilities of multimodal models based solely on their performance on out-of-distribution data fails to capture their true robustness. This work intr…
Enhancing Continual Learning in Visual Question Answering with Modality-Aware Feature Distillation
Malvina Nikandrou, Georgios Pantazopoulos, Ioannis Konstas +1
Continual learning focuses on incrementally training a model on a sequence of tasks with the aim of learning new tasks while minimizing performance drop on previous tasks. Existing…
AlanaVLM: A Multimodal Embodied AI Foundation Model for Egocentric Video Understanding
Alessandro Suglia, Claudio Greco, Katie Baker +5
AI personal assistants deployed via robots or wearables require embodied understanding to collaborate with humans effectively. However, current Vision-Language Models (VLMs) primar…
Visually Grounded Language Learning: a review of language games, datasets, tasks, and models
Alessandro Suglia, Ioannis Konstas, Oliver Lemon
In recent years, several machine learning models have been proposed. They are trained with a language modelling objective on large-scale text-only data. With such pretraining, they…