6 citations · 8 across the 9 of their papers we have counts for
10 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…
"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…
Feature Generation Using LLMs: An Evolutionary Algorithm Approach
Aria Nourbakhsh, Benoît Alcaraz, Christoph Schommer
A crucial step in machine learning pipelines is to present each entity with features or attributes that are representative of the characteristics of the processed entities. Feature…
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…
Cluster Purge Loss: Structuring Transformer Embeddings for Equivalent Mutants Detection
Adelaide Danilov, Aria Nourbakhsh, Christoph Schommer
Recent pre-trained transformer models achieve superior performance in various code processing objectives. However, although effective at optimizing decision boundaries, common appr…
The Dark Side of the Language: Pre-trained Transformers in the DarkNet
Leonardo Ranaldi, Aria Nourbakhsh, Arianna Patrizi +4
Pre-trained Transformers are challenging human performances in many NLP tasks. The massive datasets used for pre-training seem to be the key to their success on existing tasks. In…
Toward Dialogue Modeling: A Semantic Annotation Scheme for Questions and Answers
Maria-Andrea Cruz-Blandón, Gosse Minnema, Aria Nourbakhsh +2
The present study proposes an annotation scheme for classifying the content and discourse contribution of question-answer pairs. We propose detailed guidelines for using the scheme…