1 citations · 1 across the 2 of their papers we have counts for
9 papers
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,…