4 citations · 7 across the 5 of their papers we have counts for
5 papers
Consensus as Privileged Context for Label-Free Self-Distillation
John Gkountouras, Josip Jukić, Ivan Titov
Sampling multiple solutions and returning the majority answer is among the most reliable ways to improve the reasoning accuracy of large language models without labels, and a growi…
Clarification as Supervision: Reinforcement Learning for Vision-Language Interfaces
John Gkountouras, Ivan Titov
Recent text-only models demonstrate remarkable mathematical reasoning capabilities. Extending these to visual domains requires vision-language models to translate images into text…
Language Agents Meet Causality -- Bridging LLMs and Causal World Models
John Gkountouras, Matthias Lindemann, Phillip Lippe +2
Large Language Models (LLMs) have recently shown great promise in planning and reasoning applications. These tasks demand robust systems, which arguably require a causal understand…
INT2.1: Towards Fine-Tunable Quantized Large Language Models with Error Correction through Low-Rank Adaptation
Yuji Chai, John Gkountouras, Glenn G. Ko +2
We introduce a method that dramatically reduces fine-tuning VRAM requirements and rectifies quantization errors in quantized Large Language Models. First, we develop an extremely m…
VISION DIFFMASK: Faithful Interpretation of Vision Transformers with Differentiable Patch Masking
Angelos Nalmpantis, Apostolos Panagiotopoulos, John Gkountouras +2
The lack of interpretability of the Vision Transformer may hinder its use in critical real-world applications despite its effectiveness. To overcome this issue, we propose a post-h…