SOTAVerified

Explanation Generation

Papers

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Showing 1–50 of 235 papers

TitleStatusHype
Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face DetectorCode2
MACRec: a Multi-Agent Collaboration Framework for RecommendationCode2
Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer's Disease Diagnosis ModelCode1
Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasksCode1
Towards Explainable Conversational Recommender SystemsCode1
LLMRec: Benchmarking Large Language Models on Recommendation TaskCode1
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language ExplanationsCode1
Explain and Predict, and then Predict AgainCode1
OCTET: Object-aware Counterfactual ExplanationsCode1
Sim2Word: Explaining Similarity with Representative Attribute Words via Counterfactual ExplanationsCode1
XplainLLM: A Knowledge-Augmented Dataset for Reliable Grounded Explanations in LLMsCode1
Towards Interpretable Natural Language Understanding with Explanations as Latent VariablesCode1
End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and ModelsCode1
QED: A Framework and Dataset for Explanations in Question AnsweringCode1
EX-FEVER: A Dataset for Multi-hop Explainable Fact VerificationCode1
Explaining black box text modules in natural language with language modelsCode1
Faithfully Explainable Recommendation via Neural Logic ReasoningCode1
Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation GenerationCode1
LLM4Vis: Explainable Visualization Recommendation using ChatGPTCode1
Multi-source Semantic Graph-based Multimodal Sarcasm Explanation GenerationCode1
Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language ModelsCode1
AR-BERT: Aspect-relation enhanced Aspect-level Sentiment Classification with Multi-modal ExplanationsCode1
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and BeyondCode1
VLIS: Unimodal Language Models Guide Multimodal Language GenerationCode1
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural NetworksCode1
Variational Causal Inference Network for Explanatory Visual Question AnsweringCode1
HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-CheckingCode1
REX: Reasoning-aware and Grounded ExplanationCode1
TE2Rules: Explaining Tree Ensembles using RulesCode1
Do Vision & Language Decoders use Images and Text equally? How Self-consistent are their Explanations?Code1
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question AnsweringCode1
Retrieval augmentation of large language models for lay language generationCode1
Explainable Automated Fact-Checking for Public Health ClaimsCode1
Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale ExtractionCode1
Explaining Patterns in Data with Language Models via Interpretable AutopromptingCode1
CLEVR-X: A Visual Reasoning Dataset for Natural Language ExplanationsCode1
CodeExp: Explanatory Code Document GenerationCode1
A Survey on Interpretable Cross-modal ReasoningCode1
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?Code1
Calibrating Trust of Multi-Hop Question Answering Systems with Decompositional Probes—0
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images—0
Are Training Resources Insufficient? Predict First Then Explain!—0
Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models—0
Active entailment encoding for explanation tree construction using parsimonious generation of hard negatives—0
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning—0
Efficiently Explaining CSPs with Unsatisfiable Subset Optimization (extended algorithms and examples)—0
Best of Both Worlds: A Hybrid Approach for Multi-Hop Explanation with Declarative Facts—0
EGCR: Explanation Generation for Conversational Recommendation—0
Do Natural Language Explanations Represent Valid Logical Arguments? Verifying Entailment in Explainable NLI Gold Standards—0
Balancing Explicability and Explanation in Human-Aware Planning—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