Papers Explanation Generation
“Explanation Generation” 태그가 달린 논문 307편 · 필터 해제
Selecting The Most Informative Tokens in Natural Language Autoencoders
Natural language autoencoders translate a language model's internal activations into readable explanations. Explaining every token position is costly. Which positions should an auditor inspect to understand a potential t…
Explanation GenerationOrder Matters: A Chinese Multi-Panel Meme Benchmark for Vision-Language Reasoning
Many multimodal tasks depend on how visual elements are ordered and composed, not only on recognizing them in isolation. Internet memes are a compact case of this problem: their punchline often depends on a constrained r…
Explanation GenerationMultimodal ReasoningPATE-Forensics: Perception-as-Tool for Explainable Deepfake Forensics with General-Purpose MLLMs
Existing explainable deepfake forensic methods typically rely on task-adapted MLLM to jointly address detection, localization, and explanation. Inspired by agent-style tool use, we instead introduce a Perception-as-Tool …
Explanation GenerationComputational KJ-Ho: An Analyst-Bias-Free Insight Extraction Framework from Large-Scale Qualitative Data Using Domain-Specialized LLMs
The qualitative research methodologies that underpin consumer-insight generation - the KJ method, Grounded Theory, and Thematic Analysis - share a structural constraint: the cognitive processing capacity of the human ana…
Explanation GenerationArtECulture: Benchmarking Culture-Conditioned Visual Emotion Understanding in Multimodal Large Language Models
Existing visual emotion understanding methods typically ignore cultural variations in emotional perception. We introduce culture-conditioned visual emotion understanding, a task that predicts the culture-specific emotion…
Explanation GenerationHalluTruthQA-4K: A Fine-Grained Corpus and Annotation Process for Arabic Hallucination Detection and Truth Verification
Large language models can generate fluent Arabic answers while introducing factual errors that are difficult to identify and verify. Existing Arabic hallucination resources often assign a binary label to an entire respon…
Explanation GenerationQuestion SelectionAnswer GenerationClass-Aware Reinforcement Learning for Counterfactual Explanation Generation
Counterfactual explanations (CFEs) enhance the interpretability of black-box models by generating alternative instances with adjusted feature values that achieve a contrastive outcome. Reinforcement learning (RL) offers …
Explanation GenerationReinforcement LearningReasoning or Memorization: Can LLMs Understand and Generate Chinese Xiehouyu Riddles?
In this paper, we push the boundary of LLM reasoning by testing them in a Chinese language game, xiehouyu, with novel xiehouyu created by linguists that had not existed before to avoid data contamination. We use multiple…
Explanation GenerationHalluScope: Fine-grained Hallucination Diagnosis for Multimodal Large Language Models
Although Multimodal Large Language Models have achieved strong performance across a wide range of vision-language tasks, they still suffer from hallucinations, where model outputs become inconsistent with the visual cont…
Explanation GenerationExplainable Deepfake Detection Challenge
Deepfake detection is moving beyond binary classification decisions toward systems that can also explain the visual evidence supporting those decisions. This transition is important for real-world verification settings, …
Explanation GenerationBinary ClassificationImage ClassificationSemantic SimilarityTrustworthy Machine Learning through the Lens of Combinatorial Optimization: Survey and Research Perspectives
Modern machine learning (ML) increasingly relies on complex models whose behavior is difficult to characterize beyond empirical performance metrics. Across a wide range of tasks, including prediction, generation, and dec…
Explanation GenerationModel CompressionNeSy-CSA: A Neuro-Symbolic Framework for Open-Ended Critical Scenario Attribution
Understanding why discovered scenarios become critical in scenario-based testing is essential for effectively leveraging them in decision-making systems. Reasoning about such criticality can be formulated as an attributi…
Explanation GenerationBi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search
Recommender systems are vital in helping users navigate vast amounts of information, offering personalized suggestions and effective explanations for these recommendations. While previous efforts have attempted to provid…
Neural Architecture SearchExplanation GenerationClosing the Loop: Formally Verified Law as a Reward Signal for Self-Improving Legal AI
This article develops an architecture that creates a formally verifiable reward signal to train legal AI, adapting the LLM proposes, verifier disposes paradigm from mathematical AI to the distinctive demands of law. We p…
Explanation GenerationA Completion-Aware Framework for Impactful Counterfactual Explainability in Graph Neural Networks
In this study, we propose a novel pipeline for generic, model-agnostic, local-level counterfactual explainability in graph neural networks (GNNs). Although counterfactual explainers capable of both adding and removing ed…
Explanation GenerationGraph ClassificationLink PredictionXAI-Grounded Explanation Generation for Speech Deepfake Detection with Training-Free Multimodal Large Language Models
Speech deepfake detection (SDD) systems require trustworthy explanations for reliable decision-making. Existing explanation ways mainly fall into two categories. Traditional explainable AI (XAI), such as gradient-based a…
Explanation GenerationDeepFake DetectionArabiGEE: A Hierarchical Taxonomy for Arabic Grammatical Error Explanation
We introduce ArabiGEE, the first comprehensive Arabic grammatical error explanation (GEE) taxonomy grounded in explicit error types. Unlike existing GEE approaches that treat explanation generation as free-form text, Ara…
Grammatical Error CorrectionExplanation GenerationDeveloping an Intelligent Job Recommendation System Using Semantic Retrieval and Explainable AI Techniques
Online recruitment platforms require recommendation methods capable of retrieving relevant job opportunities from large and heterogeneous collections of job postings. Keyword-based search is efficient and interpretable, …
Explanation GenerationSemantic RetrievalTeaching Through Analogies: A Modular Pipeline for Educational Analogy Generation
Analogies help learners understand unfamiliar concepts by relating them to known concepts. Despite recent advances, large language models (LLMs) continue to struggle to generate analogies of comparable quality to those p…
Explanation GenerationUNR-Explainer: Counterfactual Explanations for Unsupervised Node Representation Learning Models
Node representation learning, such as Graph Neural Networks (GNNs), has emerged as a pivotal method in machine learning. The demand for reliable explanation generation surges, yet unsupervised models remain underexplored…
Representation LearningExplanation GenerationLink Prediction