16 citations · 16 across the 10 of their papers we have counts for
13 papers
Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utility
Salima Lamsiyah, Adelaide Danilov, Oleksandr Marchenko Breneur +1
Sparse autoencoders (SAEs) have emerged as a promising approach to mechanistic interpretability by decomposing language-model activations into sparse, potentially interpretable fea…
DialectSentEval 2026: Arabic Dialect Sentiment Analysis and Swapping Shared Task
Saad Ezzini, Shadi Abudalfa, Maram Alharbi +6
Sentiment analysis is a fundamental problem in Natural Language Processing (NLP). Standard sentiment classification for the Arabic language remains challenging due to the high volu…
RECTIFY: An Interactive Workbench for Post-Evaluation RAG Diagnosis, Repair, and Verification
Keerthana Murugaraj, Salima Lamsiyah, Martin Theobald
Retrieval-Augmented Generation (RAG) evaluators can identify failures such as weak retrieval, poor grounding, incomplete answers, and unsupported generation, but they rarely help d…
"Many Are My Names": The Anatomy of the Assistant and Its Personas via Sparse Autoencoders
Adelaide Danilov, Aria Nourbakhsh, Oleksandr Marchenko Breneur +1
How a language model internally represents who is speaking, the Assistant, an assigned roleplay persona, or a narrated story character, remains underexplored. We study speaker repr…
Why Current XAI Is Not Enough for Arabic NLP: A Critical Survey of the Explainability Gap
Salima Lamsiyah, Ruslan Mitkov
Explainable AI (XAI) is now a major theme in NLP; however, Arabic NLP remains under-explained in three connected senses. First, there is a method gap: Arabic XAI relies heavily on…
AEyeDE: An Attention-Based Attribution Framework for AI-Generated Text Detection
Aria Nourbakhsh, Adelaide Danilov, Christoph Schommer +1
Detecting AI-generated text is becoming increasingly challenging as modern language models approach human-level fluency and can evade detectors that rely on surface statistics or l…
CSULoRA: Closest Safe Update Low-Rank Adaptation
Oleksandr Marchenko Breneur, Adelaide Danilov, Aria Nourbakhsh +1
Low-rank adaptation has become a standard method for parameter-efficient fine-tuning of large language models, but even small amounts of unsafe or adversarial fine-tuning data can…
Evaluating Explainable AI Attribution Methods in Neural Machine Translation via Attention-Guided Knowledge Distillation
Aria Nourbakhsh, Salima Lamsiyah, Adelaide Danilov +1
The study of the attribution of input features to the output of neural network models is an active area of research. While numerous Explainable AI (XAI) techniques have been propos…
NOTAI.AI: Explainable Detection of Machine-Generated Text via Curvature and Feature Attribution
Oleksandr Marchenko Breneur, Adelaide Danilov, Aria Nourbakhsh +1
We present NotAI.AI, an explainable AI-generated text detection system. Instead of returning only a binary label or confidence score, the system shows which signals influenced the…
Automating Historical Insight Extraction from Large-Scale Newspaper Archives via Neural Topic Modeling
Keerthana Murugaraj, Salima Lamsiyah, Marten During +1
Extracting coherent and human-understandable themes from large collections of unstructured historical newspaper archives presents significant challenges due to topic evolution, Opt…
RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation
Keerthana Murugaraj, Salima Lamsiyah, Martin Theobald
Evaluating Retrieval-Augmented Generation (RAG) systems remains a challenging task: existing metrics often collapse heterogeneous behaviors into single scores and provide little in…
M-DAIGT: A Shared Task on Multi-Domain Detection of AI-Generated Text
Salima Lamsiyah, Saad Ezzini, Abdelkader El Mahdaouy +5
The generation of highly fluent text by Large Language Models (LLMs) poses a significant challenge to information integrity and academic research. In this paper, we introduce the M…
DomURLs_BERT: Pre-trained BERT-based Model for Malicious Domains and URLs Detection and Classification
Abdelkader El Mahdaouy, Salima Lamsiyah, Meryem Janati Idrissi +3
Detecting and classifying suspicious or malicious domain names and URLs is fundamental task in cybersecurity. To leverage such indicators of compromise, cybersecurity vendors and p…