7 papers
Efficient Document Tampering Localization with Multi-Level Discrepancy Features and Unified DCT-Quantization Embedding
Mohamed Dhouib, Ye Zhu, Sonia Vanier +1
Localizing document tampering is extremely challenging, as manipulations are crafted to appear visually consistent and often leave only subtle traces that are nearly invisible to t…
MUCH: A Multilingual Claim Hallucination Benchmark
Jérémie Dentan, Alexi Canesse, Davide Buscaldi +2
Claim-level Uncertainty Quantification (UQ) is a promising approach to mitigate the lack of reliability in Large Language Models (LLMs). We introduce MUCH, the first claim-level UQ…
Leveraging Contrastive Learning for a Similarity-Guided Tampered Document Data Generation Pipeline
Mohamed Dhouib, Davide Buscaldi, Sonia Vanier +1
Detecting tampered text in document images is a challenging task due to data scarcity. To address this, previous work has attempted to generate tampered documents using rule-based…
Predicting memorization within Large Language Models fine-tuned for classification
Jérémie Dentan, Davide Buscaldi, Aymen Shabou +1
Large Language Models have received significant attention due to their abilities to solve a wide range of complex tasks. However these models memorize a significant proportion of t…
PACT: Pruning and Clustering-Based Token Reduction for Faster Visual Language Models
Mohamed Dhouib, Davide Buscaldi, Sonia Vanier +1
Visual Language Models require substantial computational resources for inference due to the additional input tokens needed to represent visual information. However, these visual to…
MIX : a Multi-task Learning Approach to Solve Open-Domain Question Answering
Sofian Chaybouti, Achraf Saghe, Aymen Shabou
This paper introduces MIX, a multi-task deep learning approach to solve open-ended question-answering. First, we design our system as a multi-stage pipeline of 3 building blocks: a…