Generative Example-Based Explanations: Bridging the Gap between Generative Modeling and Explainability

• URL: http://arxiv.org/abs/2410.20890v2
• Date: Wed, 10 Sep 2025 08:43:29 GMT
• Title: Generative Example-Based Explanations: Bridging the Gap between Generative Modeling and Explainability
• Authors: Philipp Vaeth, Alexander M. Fruehwald, Benjamin Paassen, Magda Gregorova,
• Abstract summary: Several methods have leveraged deep generative modeling to produce example-based explanations of image classifiers.<n>Despite producing visually stunning results, these methods are largely disconnected from classical explainability literature.<n>This conceptual and communication gap leads to misunderstandings and misalignments in goals and expectations.
• Score: 40.26990075693758
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, several methods have leveraged deep generative modeling to produce example-based explanations of image classifiers. Despite producing visually stunning results, these methods are largely disconnected from classical explainability literature. This conceptual and communication gap leads to misunderstandings and misalignments in goals and expectations. In this paper, we bridge this gap by proposing a probabilistic framework for example-based explanations, formally defining the example-based explanations in a probabilistic manner amenable for modeling via deep generative models while coherent with the critical characteristics and desiderata widely accepted in the explainability community. Our aim is on one hand to provide a constructive framework for the development of well-grounded generative algorithms for example-based explanations and, on the other, to facilitate communication between the generative and explainability research communities, foster rigor and transparency, and improve the quality of peer discussion and research progress in this promising direction.

Related papers

• Preference Learning for AI Alignment: a Causal Perspective [55.2480439325792]
  We frame this problem in a causal paradigm, providing the rich toolbox of causality to identify persistent challenges.<n>Inheriting from the literature of causal inference, we identify key assumptions necessary for reliable generalisation.<n>We illustrate failure modes of naive reward models and demonstrate how causally-inspired approaches can improve model robustness.
  arXiv  Detail & Related papers  (2025-06-06T10:45:42Z)
• Causality can systematically address the monsters under the bench(marks) [64.36592889550431]
  Benchmarks are plagued by various biases, artifacts, or leakage.<n>Models may behave unreliably due to poorly explored failure modes.<n> causality offers an ideal framework to systematically address these challenges.
  arXiv  Detail & Related papers  (2025-02-07T17:01:37Z)
• Interpreting Inflammation Prediction Model via Tag-based Cohort Explanation [5.356481722174994]
  We propose a novel framework for identifying cohorts within a dataset based on local feature importance scores. We evaluate our framework on a food-based inflammation prediction model and demonstrated that the framework can generate reliable explanations that match domain knowledge.
  arXiv  Detail & Related papers  (2024-10-17T23:22:59Z)
• On Generating Monolithic and Model Reconciling Explanations in Probabilistic Scenarios [46.24262986854885]
  We propose a novel framework for generating probabilistic monolithic explanations and model reconciling explanations.<n>For monolithic explanations, our approach integrates uncertainty by utilizing probabilistic logic to increase the probability of the explanandum.<n>For model reconciling explanations, we propose a framework that extends the logic-based variant of the model reconciliation problem to account for probabilistic human models.
  arXiv  Detail & Related papers  (2024-05-29T16:07:31Z)
• Towards a Unified Framework for Evaluating Explanations [0.6138671548064356]
  We argue that explanations serve as mediators between models and stakeholders, whether for intrinsically interpretable models or opaque black-box models. We illustrate these criteria, as well as specific evaluation methods, using examples from an ongoing study of an interpretable neural network for predicting a particular learner behavior.
  arXiv  Detail & Related papers  (2024-05-22T21:49:28Z)
• Explaining Text Similarity in Transformer Models [52.571158418102584]
  Recent advances in explainable AI have made it possible to mitigate limitations by leveraging improved explanations for Transformers. We use BiLRP, an extension developed for computing second-order explanations in bilinear similarity models, to investigate which feature interactions drive similarity in NLP models. Our findings contribute to a deeper understanding of different semantic similarity tasks and models, highlighting how novel explainable AI methods enable in-depth analyses and corpus-level insights.
  arXiv  Detail & Related papers  (2024-05-10T17:11:31Z)
• Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales [3.242050660144211]
  Saliency post-hoc explainability methods are important tools for understanding increasingly complex NLP models. We present a methodology for incorporating rationales, which are text annotations explaining human decisions, into text classification models.
  arXiv  Detail & Related papers  (2024-04-03T22:39:33Z)
• Explainability for Large Language Models: A Survey [59.67574757137078]
  Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. This paper introduces a taxonomy of explainability techniques and provides a structured overview of methods for explaining Transformer-based language models.
  arXiv  Detail & Related papers  (2023-09-02T22:14:26Z)
• Advanced Conditional Variational Autoencoders (A-CVAE): Towards interpreting open-domain conversation generation via disentangling latent feature representation [15.742077523458995]
  This paper proposes to harness the generative model with a priori knowledge through a cognitive approach involving mesoscopic scale feature disentanglement. We propose a new metric for open-domain dialogues, which can objectively evaluate the interpretability of the latent space distribution.
  arXiv  Detail & Related papers  (2022-07-26T07:39:36Z)
• Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization [73.89239820192894]
  We argue that automated counterfactual generation should regard several aspects of the produced adversarial instances. We present a novel framework for the generation of counterfactual examples.
  arXiv  Detail & Related papers  (2022-05-20T15:02:53Z)
• Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models [77.34726150561087]
  We define explainability through the interpretability of the explanations and the faithfulness of the explainability model in the field of process outcome prediction. This paper contributes a set of guidelines named X-MOP which allows selecting the appropriate model based on the event log specifications.
  arXiv  Detail & Related papers  (2022-03-30T05:59:50Z)
• Representations of epistemic uncertainty and awareness in data-driven strategies [0.0]
  We present a theoretical model for uncertainty in knowledge representation and its transfer mediated by agents. We look at inequivalent knowledge representations in terms of inferences, preference relations, and information measures. We discuss some implications of the proposed model for data-driven strategies.
  arXiv  Detail & Related papers  (2021-10-21T21:18:21Z)
• On the Connections between Counterfactual Explanations and Adversarial Examples [14.494463243702908]
  We make one of the first attempts at formalizing the connections between counterfactual explanations and adversarial examples. Our analysis demonstrates that several popular counterfactual explanation and adversarial example generation methods are equivalent. We empirically validate our theoretical findings using extensive experimentation with synthetic and real world datasets.
  arXiv  Detail & Related papers  (2021-06-18T08:22:24Z)
• Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation [51.29486247405601]
  We design a framework to generate counterfactuals for raw data instances with the proposed Attribute-Informed Perturbation (AIP) By utilizing generative models conditioned with different attributes, counterfactuals with desired labels can be obtained effectively and efficiently. Experimental results on real-world texts and images demonstrate the effectiveness, sample quality as well as efficiency of our designed framework.
  arXiv  Detail & Related papers  (2021-01-18T08:37:13Z)
• Enhancing Dialogue Generation via Multi-Level Contrastive Learning [57.005432249952406]
  We propose a multi-level contrastive learning paradigm to model the fine-grained quality of the responses with respect to the query. A Rank-aware (RC) network is designed to construct the multi-level contrastive optimization objectives. We build a Knowledge Inference (KI) component to capture the keyword knowledge from the reference during training and exploit such information to encourage the generation of informative words.
  arXiv  Detail & Related papers  (2020-09-19T02:41:04Z)
• Generalization Properties of Optimal Transport GANs with Latent Distribution Learning [52.25145141639159]
  We study how the interplay between the latent distribution and the complexity of the pushforward map affects performance. Motivated by our analysis, we advocate learning the latent distribution as well as the pushforward map within the GAN paradigm.
  arXiv  Detail & Related papers  (2020-07-29T07:31:33Z)
• Adversarial Mutual Information for Text Generation [62.974883143784616]
  We propose Adversarial Mutual Information (AMI): a text generation framework. AMI is formed as a novel saddle point (min-max) optimization aiming to identify joint interactions between the source and target. We show that AMI has potential to lead to a tighter lower bound of maximum mutual information.
  arXiv  Detail & Related papers  (2020-06-30T19:11:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.