27 citations · 38 across the 11 of their papers we have counts for
11 papers
Concept-Level Explainability for Auditing & Steering LLM Responses
Kenza Amara, Rita Sevastjanova, Mennatallah El-Assady
As large language models (LLMs) become widely deployed, concerns about their safety and alignment grow. An approach to steer LLM behavior, such as mitigating biases or defending ag…
Why context matters in VQA and Reasoning: Semantic interventions for VLM input modalities
Kenza Amara, Lukas Klein, Carsten Lüth +3
The various limitations of Generative AI, such as hallucinations and model failures, have made it crucial to understand the role of different modalities in Visual Language Model (V…
iToT: An Interactive System for Customized Tree-of-Thought Generation
Alan Boyle, Isha Gupta, Sebastian Hönig +4
As language models have become increasingly successful at a wide array of tasks, different prompt engineering methods have been developed alongside them in order to adapt these mod…
Challenges and Opportunities in Text Generation Explainability
Kenza Amara, Rita Sevastjanova, Mennatallah El-Assady
The necessity for interpretability in natural language processing (NLP) has risen alongside the growing prominence of large language models. Among the myriad tasks within NLP, text…
SyntaxShap: Syntax-aware Explainability Method for Text Generation
Kenza Amara, Rita Sevastjanova, Mennatallah El-Assady
To harness the power of large language models in safety-critical domains, we need to ensure the explainability of their predictions. However, despite the significant attention to m…
PowerGraph: A power grid benchmark dataset for graph neural networks
Anna Varbella, Kenza Amara, Blazhe Gjorgiev +2
Power grids are critical infrastructures of paramount importance to modern society and, therefore, engineered to operate under diverse conditions and failures. The ongoing energy t…
Generative Explanations for Graph Neural Network: Methods and Evaluations
Jialin Chen, Kenza Amara, Junchi Yu +1
Graph Neural Networks (GNNs) achieve state-of-the-art performance in various graph-related tasks. However, the black-box nature often limits their interpretability and trustworthin…
GInX-Eval: Towards In-Distribution Evaluation of Graph Neural Network Explanations
Kenza Amara, Mennatallah El-Assady, Rex Ying
Diverse explainability methods of graph neural networks (GNN) have recently been developed to highlight the edges and nodes in the graph that contribute the most to the model predi…
GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks
Kenza Amara, Rex Ying, Zitao Zhang +5
As one of the most popular machine learning models today, graph neural networks (GNNs) have attracted intense interest recently, and so does their explainability. Users are increas…
ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery
Gyri Reiersen, David Dao, Björn Lütjens +5
Forest biomass is a key influence for future climate, and the world urgently needs highly scalable financing schemes, such as carbon offsetting certifications, to protect and resto…
Nearest neighbor search with compact codes: A decoder perspective
Kenza Amara, Matthijs Douze, Alexandre Sablayrolles +1
Modern approaches for fast retrieval of similar vectors on billion-scaled datasets rely on compressed-domain approaches such as binary sketches or product quantization. These metho…