SOTAVerified

Explanation Generation

Papers

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Showing 51–100 of 235 papers

TitleStatusHype
Graph-Guided Textual Explanation Generation Framework—0
Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems—0
Automatic Claim Review for Climate Science via Explanation Generation—0
Efficiently Explaining CSPs with Unsatisfiable Subset Optimization (extended algorithms and examples)—0
EGCR: Explanation Generation for Conversational Recommendation—0
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning—0
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning—0
Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models—0
Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap—0
Enhancing Emotion Prediction in News Headlines: Insights from ChatGPT and Seq2Seq Models for Free-Text Generation—0
Enriching Visual with Verbal Explanations for Relational Concepts -- Combining LIME with Aleph—0
Calibrating Trust of Multi-Hop Question Answering Systems with Decompositional Probes—0
Generating Skyline Explanations for Graph Neural Networks—0
Harnessing LLMs Explanations to Boost Surrogate Models in Tabular Data Classification—0
Hierarchical Aspect-guided Explanation Generation for Explainable Recommendation—0
Implementing Evidential Reasoning in Expert Systems—0
Generating Contrastive Explanations for Inductive Logic Programming Based on a Near Miss Approach—0
A Framework of Explanation Generation toward Reliable Autonomous Robots—0
Generating Explanations in Medical Question-Answering by Expectation Maximization Inference over Evidence—0
Creating an Explainable Intrusion Detection System Using Self Organizing Maps—0
Augmenting the Veracity and Explanations of Complex Fact Checking via Iterative Self-Revision with LLMs—0
Generating Commonsense Explanation by Extracting Bridge Concepts from Reasoning Paths—0
Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing—0
Counterfactual Explanations for Predictive Business Process Monitoring—0
A Transparency Paradox? Investigating the Impact of Explanation Specificity and Autonomous Vehicle Perceptual Inaccuracies on Passengers—0
A Framework for Rationale Extraction for Deep QA models—0
A Deep Generative XAI Framework for Natural Language Inference Explanations Generation—0
Explaining latent representations of generative models with large multimodal models—0
Coherency Improved Explainable Recommendation via Large Language Model—0
A Three-step Method for Multi-Hop Inference Explanation Regeneration—0
Generally-Occurring Model Change for Robust Counterfactual Explanations—0
Counterfactual Explanation Generation with s(CASP)—0
Explaining with Attribute-based and Relational Near Misses: An Interpretable Approach to Distinguishing Facial Expressions of Pain and Disgust—0
Explanation as a Defense of Recommendation—0
Explanation, Debate, Align: A Weak-to-Strong Framework for Language Model Generalization—0
Explanation Generation for a Math Word Problem Solver—0
Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming—0
CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations—0
Explanations for CommonsenseQA: New Dataset and Models—0
Explanations from Large Language Models Make Small Reasoners Better—0
Exploring Effectiveness of Explanations for Appropriate Trust: Lessons from Cognitive Psychology—0
Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions—0
Diagnostics-Guided Explanation Generation—0
Generate Natural Language Explanations for Recommendation—0
FASTER-CE: Fast, Sparse, Transparent, and Robust Counterfactual Explanations—0
Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering—0
Fine-tuning BERT with Focus Words for Explanation Regeneration—0
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention—0
Do Natural Language Explanations Represent Valid Logical Arguments? Verifying Entailment in Explainable NLI Gold Standards—0
Explaining Competitive-Level Programming Solutions using LLMs—0
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All datasetsWHOOPS!CLEVR-Xe-SNLI-VEVCRVQA-X

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VLIS (Lynx)Accuracy80—Unverified
2VLIS (LLaVA)Accuracy73—Unverified
3Ground-truth Caption -> GPT3 (Oracle)Human (%)68—Unverified
4Predicted Caption -> GPT3Human (%)33—Unverified
5BLIP2 FlanT5-XXL (Fine-tuned)Human (%)27—Unverified
6BLIP2 FlanT5-XL (Fine-tuned)Human (%)15—Unverified
7BLIP2 FlanT5-XXL (Zero-shot)Human (%)0—Unverified
#ModelMetricClaimedVerifiedStatus
1PJ-XB487.4—Unverified
2FMB478.8—Unverified
#ModelMetricClaimedVerifiedStatus
1OFA-XHuman Explanation Rating85.7—Unverified
2OFA-X-MTHuman Explanation Rating80.4—Unverified
#ModelMetricClaimedVerifiedStatus
1OFA-X-MTHuman Explanation Rating77.3—Unverified
2OFA-XHuman Explanation Rating68.9—Unverified
#ModelMetricClaimedVerifiedStatus
1OFA-XHuman Explanation Rating89.5—Unverified
2OFA-X-MTHuman Explanation Rating87.8—Unverified