12 citations · 25 across the 9 of their papers we have counts for
10 papers
Targeted Visual Counterfactual Explanations for Contrastive Vision-Language Model
Van Bach Nguyen, Jörg Schlötterer, Christin Seifer
Current explanation methods for contrastive vision-language models such as CLIP mainly identify important regions without showing how to change the input in order to get a target p…
Judge Circuits Explain Format-Induced Inconsistency in LLM-as-a-Judge
Nils Feldhus, Tanja Baeumel, Elena Golimblevskaia +10
LLM-as-a-judge has become the dominant paradigm for grading model outputs at scale, yet the same model assigns systematically different scores when its output format changes (e.g.,…
Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation
Qianli Wang, Van Bach Nguyen, Yihong Liu +6
Counterfactuals refer to minimally edited inputs that cause a model's prediction to change, serving as a promising approach to explaining the model's behavior. Large language model…
Truth or Twist? Optimal Model Selection for Reliable Label Flipping Evaluation in LLM-based Counterfactuals
Qianli Wang, Van Bach Nguyen, Nils Feldhus +4
Counterfactual examples are widely employed to enhance the performance and robustness of large language models (LLMs) through counterfactual data augmentation (CDA). However, the s…
Guiding LLMs to Generate High-Fidelity and High-Quality Counterfactual Explanations for Text Classification
Van Bach Nguyen, Christin Seifert, Jörg Schlötterer
The need for interpretability in deep learning has driven interest in counterfactual explanations, which identify minimal changes to an instance that change a model's prediction. C…
LLMs for Generating and Evaluating Counterfactuals: A Comprehensive Study
Van Bach Nguyen, Paul Youssef, Christin Seifert +1
As NLP models become more complex, understanding their decisions becomes more crucial. Counterfactuals (CFs), where minimal changes to inputs flip a model's prediction, offer a way…
CEval: A Benchmark for Evaluating Counterfactual Text Generation
Van Bach Nguyen, Jörg Schlötterer, Christin Seifert
Counterfactual text generation aims to minimally change a text, such that it is classified differently. Judging advancements in method development for counterfactual text generatio…
From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent
Van Bach Nguyen, Jörg Schlötterer, Christin Seifert
The goal of Explainable AI (XAI) is to design methods to provide insights into the reasoning process of black-box models, such as deep neural networks, in order to explain them to…
The Importance of Future Information in Credit Card Fraud Detection
Van Bach Nguyen, Kanishka Ghosh Dastidar, Michael Granitzer +1
Fraud detection systems (FDS) mainly perform two tasks: (i) real-time detection while the payment is being processed and (ii) posterior detection to block the card retrospectively…
Policy Learning for Malaria Control
Van Bach Nguyen, Belaid Mohamed Karim, Bao Long Vu +2
Sequential decision making is a typical problem in reinforcement learning with plenty of algorithms to solve it. However, only a few of them can work effectively with a very small…