1 citations · 2 across the 9 of their papers we have counts for
9 papers
MemoryLACE: Memory Lifecycle-Aware Consolidation and Evidence Retrieval
Meriem Yacoubi, Pia Schmidt, Nenad Petrovic +3
Long-term LLM agents must preserve information across interactions while distinguishing repeated evidence, historical states, updates, and unresolved contradictions. Existing textu…
Jais 2: A Family of Arabic-Centric Open Large Language Models
Mohamed Anwar, Abed Alhakim Freihat, George Ibrahim +57
Jais 2 is a family of Arabic-centric large language models developed jointly by MBZUAI, Cerebras, and Inception, designed to advance Arabic-centric language modeling, with strong p…
PrivacyScalpel: Enhancing LLM Privacy via Interpretable Feature Intervention with Sparse Autoencoders
Ahmed Frikha, Muhammad Reza Ar Razi, Krishna Kanth Nakka +3
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing but also pose significant privacy risks by memorizing and leaking Personally I…
One-Class Domain Adaptation via Meta-Learning
Stephanie Holly, Thomas Bierweiler, Stefan von Dosky +3
The deployment of IoT (Internet of Things) sensor-based machine learning models in industrial systems for anomaly classification tasks poses significant challenges due to distribut…
PII-Scope: A Comprehensive Study on Training Data PII Extraction Attacks in LLMs
Krishna Kanth Nakka, Ahmed Frikha, Ricardo Mendes +2
In this work, we introduce PII-Scope, a comprehensive benchmark designed to evaluate state-of-the-art methodologies for PII extraction attacks targeting LLMs across diverse threat…
ObfuscaTune: Obfuscated Offsite Fine-tuning and Inference of Proprietary LLMs on Private Datasets
Ahmed Frikha, Nassim Walha, Ricardo Mendes +3
This work addresses the timely yet underexplored problem of performing inference and finetuning of a proprietary LLM owned by a model provider entity on the confidential/private da…
IncogniText: Privacy-enhancing Conditional Text Anonymization via LLM-based Private Attribute Randomization
Ahmed Frikha, Nassim Walha, Krishna Kanth Nakka +3
In this work, we address the problem of text anonymization where the goal is to prevent adversaries from correctly inferring private attributes of the author, while keeping the tex…
PII-Compass: Guiding LLM training data extraction prompts towards the target PII via grounding
Krishna Kanth Nakka, Ahmed Frikha, Ricardo Mendes +2
The latest and most impactful advances in large models stem from their increased size. Unfortunately, this translates into an improved memorization capacity, raising data privacy c…
Towards Artwork Explanation in Large-scale Vision Language Models
Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2
Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…