1 citations · 2 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…