4 citations · 6 across the 9 of their papers we have counts for
12 papers
Diversified and Perceptible Counterfactual Examples Leveraging Expert Knowledge
Akram Bensalem, Fahima Djelil, Marie-Jeanne Lesot +1
CounterFactual Examples (CFEs) are a cornerstone of eXplainable Artificial Intelligence (XAI), offering local, post hoc, and model-agnostic explanations by identifying minimal inpu…
Aggregative Semantics for Quantitative Bipolar Argumentation Frameworks
Yann Munro, Isabelle Bloch, Marie-Jeanne Lesot
Formal argumentation is being used increasingly in artificial intelligence as an effective and understandable way to model potentially conflicting pieces of information, called arg…
Alignment Reduces Expressed but Not Encoded Gender Bias: A Unified Framework and Study
Nour Bouchouchi, Thibault Laugel, Xavier Renard +3
During training, Large Language Models (LLMs) learn social regularities that can lead to gender bias in downstream applications. Most mitigation efforts focus on reducing bias in g…
Metric assessment protocol in the context of answer fluctuation on MCQ tasks
Ekaterina Goliakova, Xavier Renard, Marie-Jeanne Lesot +3
Using multiple-choice questions (MCQs) has become a standard for assessing LLM capabilities efficiently. A variety of metrics can be employed for this task. However, previous resea…
NeuroFaith: Evaluating LLM Self-Explanation Faithfulness via Internal Representation Alignment
Milan Bhan, Jean-Noel Vittaut, Nicolas Chesneau +2
Large Language Models (LLMs) can generate plausible free text self-explanations to justify their answers. However, these natural language explanations may not accurately reflect th…
Towards Achieving Concept Completeness for Textual Concept Bottleneck Models
Milan Bhan, Yann Choho, Pierre Moreau +3
Textual Concept Bottleneck Models (TCBMs) are interpretable-by-design models for text classification that predict a set of salient concepts before making the final prediction. This…
An action language-based formalisation of an abstract argumentation framework
Yann Munro, Camilo Sarmiento, Isabelle Bloch +3
An abstract argumentation framework is a commonly used formalism to provide a static representation of a dialogue. However, the order of enunciation of the arguments in an argument…
Why do explanations fail? A typology and discussion on failures in XAI
Clara Bove, Thibault Laugel, Marie-Jeanne Lesot +2
As Machine Learning models achieve unprecedented levels of performance, the XAI domain aims at making these models understandable by presenting end-users with intelligible explanat…
Mitigating Text Toxicity with Counterfactual Generation
Milan Bhan, Jean-Noel Vittaut, Nina Achache +5
Toxicity mitigation consists in rephrasing text in order to remove offensive or harmful meaning. Neural natural language processing (NLP) models have been widely used to target and…
Self-AMPLIFY: Improving Small Language Models with Self Post Hoc Explanations
Milan Bhan, Jean-Noel Vittaut, Nicolas Chesneau +1
Incorporating natural language rationales in the prompt and In-Context Learning (ICL) have led to a significant improvement of Large Language Models (LLMs) performance. However, ge…
Dynamic Interpretability for Model Comparison via Decision Rules
Adam Rida, Marie-Jeanne Lesot, Xavier Renard +1
Explainable AI (XAI) methods have mostly been built to investigate and shed light on single machine learning models and are not designed to capture and explain differences between…
Integrating Temporality and Causality into Acyclic Argumentation Frameworks using a Transition System
Y. Munro, C. Sarmiento, I. Bloch +2
In the context of abstract argumentation, we present the benefits of considering temporality, i.e. the order in which arguments are enunciated, as well as causality. We propose a f…