✨Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations

📝 Summary:
This paper introduces the RFxG taxonomy to categorize saliency map explanations by reference-frame and granularity. It proposes novel faithfulness metrics to improve evaluation, aiming to align explanations with diverse user intent and human understanding.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13081
• PDF: https://arxiv.org/pdf/2511.13081

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#ExplainableAI #SaliencyMaps #CognitiveScience #AIEvaluation #AIResearch

✨Fidelity-Aware Recommendation Explanations via Stochastic Path Integration

📝 Summary:
SPINRec improves recommendation explanation fidelity by using stochastic path integration and baseline sampling, capturing both observed and unobserved interactions. It consistently outperforms prior methods, setting a new benchmark for faithful explainability in recommender systems.

🔹 Publication Date: Published on Nov 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18047
• PDF: https://arxiv.org/pdf/2511.18047

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#RecommenderSystems #ExplainableAI #MachineLearning #AI #DataScience

✨REFLEX: Self-Refining Explainable Fact-Checking via Disentangling Truth into Style and Substance

📝 Summary:
REFLEX is a new fact-checking method that uses internal model knowledge to improve verdict accuracy and explanation quality. It disentangles truth into style and substance via adaptive activation signals, achieving state-of-the-art performance with minimal training data. This approach also shows ...

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20233
• PDF: https://arxiv.org/pdf/2511.20233

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#FactChecking #ExplainableAI #MachineLearning #AI #NLP

✨Show me the evidence: Evaluating the role of evidence and natural language explanations in AI-supported fact-checking

📝 Summary:
This study found that non-expert users consistently relied on evidence to validate AI claims in fact-checking. While natural language explanations reduced evidence use, participants still turned to evidence if explanations seemed flawed or insufficient. Evidence is a key ingredient for evaluating...

🔹 Publication Date: Published on Jan 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11387
• PDF: https://arxiv.org/pdf/2601.11387

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#AI #FactChecking #ExplainableAI #Evidence #InformationCredibility

✨Transformer Explainer: Interactive Learning of Text-Generative Models

📝 Summary:
Transformer Explainer is an interactive web tool enabling non-experts to understand GPT-2's internal workings. It visualizes how the model generates text in real-time based on user input. This improves public access to learning about modern generative AI.

🔹 Publication Date: Published on Aug 8, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2408.04619
• PDF: https://arxiv.org/pdf/2408.04619
• Project Page: https://poloclub.github.io/transformer-explainer/
• Github: https://github.com/helblazer811/ManimML

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#AI #ExplainableAI #LLM #DataVisualization #GenerativeAI

✨Causal Concept Graphs in LLM Latent Space for Stepwise Reasoning

📝 Summary:
Causal Concept Graphs identify causal relationships between concepts in LLMs using sparse autoencoders and differentiable structure learning. This method significantly improves causal fidelity for multi-step reasoning over prior techniques, yielding sparse and stable graphs.

🔹 Publication Date: Published on Mar 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10377
• PDF: https://arxiv.org/pdf/2603.10377

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#CausalAI #LLMs #MachineLearning #GraphLearning #ExplainableAI

✨Dr. SHAP-AV: Decoding Relative Modality Contributions via Shapley Attribution in Audio-Visual Speech Recognition

📝 Summary:
Dr. SHAP-AV uses Shapley values to analyze audio-visual speech recognition modality contributions. Findings show models shift toward visual under noise but maintain a persistent audio bias. This method serves as a key diagnostic tool for AVSR.

🔹 Publication Date: Published on Mar 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12046
• PDF: https://arxiv.org/pdf/2603.12046
• Project Page: https://umbertocappellazzo.github.io/Dr-SHAP-AV/
• Github: https://github.com/umbertocappellazzo/Dr-SHAP-AV

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#AVSR #ShapleyValues #ExplainableAI #MultimodalAI #SpeechRecognition

✨Anatomy of a Lie: A Multi-Stage Diagnostic Framework for Tracing Hallucinations in Vision-Language Models

📝 Summary:
Vision-Language Models (VLMs) frequently "hallucinate" - generate plausible yet factually incorrect statements - posing a critical barrier to their trustworthy deployment. In this work, we propose a n...

🔹 Publication Date: Published on Mar 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.15557
• PDF: https://arxiv.org/pdf/2603.15557
• Github: https://github.com/Lexiang-Xiong/CAD

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#VLM #AIHallucinations #TrustworthyAI #ExplainableAI #AIResearch

✨Progressive Training for Explainable Citation-Grounded Dialogue: Reducing Hallucination to Zero in English-Hindi LLMs

📝 Summary:
XKD-Dial is a progressive training pipeline for explainable, bilingual English-Hindi knowledge-grounded dialogue. It achieves zero hallucination rates by using citation grounding and improves explainability through post-hoc analyses.

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.18911
• PDF: https://arxiv.org/pdf/2603.18911

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#LLMs #ExplainableAI #NaturalLanguageProcessing #AIResearch #HallucinationReduction

🔥 Transformer Explainer: Interactive Learning of Text-Generative Models

💡 The paper introduces Transformer Explainer, an interactive visualization tool that helps non-experts understand the inner workings of the GPT-2 model. The problem addressed is that Transformers, despite being a revolutionary machine learning technology, are often opaque to those without extensive expertise. To tackle this issue, the authors developed a tool that provides a model overview and allows users to smoothly transition across different abstraction levels of mathematical operations and model structures.

The method used to create the tool involves integrating a live GPT-2 instance that runs locally in the user's browser, enabling users to experiment with their own input and observe in real-time how the internal components and parameters of the Transformer work together to predict the next tokens. This approach allows users to gain hands-on experience and intuition about complex Transformer concepts without requiring installation or special hardware.

The results of this work are a publicly available, open-sourced tool that broadens access to education on modern generative AI techniques. The tool is accessible at a provided website and a video demo is also available, showcasing the tool's capabilities. Overall, the paper contributes to making Transformers more accessible and understandable to a wider audience, including non-experts, by providing an interactive and intuitive learning experience.

📅 Published on Aug 8, 2024

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2408.04619
• PDF: https://arxiv.org/pdf/2408.04619
• Project Page: https://poloclub.github.io/transformer-explainer/

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#TransformerModels #GPT2Explained #NaturalLanguageProcessing #TextGenerationModels #ExplainableAI