Model Hallucination Detection Measure
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A Model Hallucination Detection Measure is a model evaluation measure that is a model factuality metric designed to identify model hallucinations through model output verification techniques.
- AKA: Model Factual Inconsistency Metric, Model Hallucination Score, Model Faithfulness Measure.
- Context:
- It can typically detect Model Factual Hallucinations through knowledge base verification and source checking.
- It can typically identify Model Intrinsic Hallucinations using self-contradiction detection and consistency checking.
- It can typically measure Model Extrinsic Hallucinations via external knowledge validation and fact verification.
- It can typically assess Model Semantic Hallucinations through meaning preservation analysis and entailment checking.
- It can typically quantify Model Hallucination Severity using error impact scoring and risk assessment.
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- It can often employ Reference-Based Model Detection comparing against ground truth documents.
- It can often utilize Model-Based Detection using trained hallucination classifiers.
- It can often implement Uncertainty-Based Model Detection through confidence score analysis.
- It can often leverage Consistency-Based Model Detection via multiple generation comparison.
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- It can range from being a Binary Model Hallucination Detection Measure to being a Graded Model Hallucination Detection Measure, depending on its scoring granularity.
- It can range from being a Domain-Specific Model Hallucination Detection Measure to being a General Model Hallucination Detection Measure, depending on its application scope.
- It can range from being an Automated Model Hallucination Detection Measure to being a Human-Assisted Model Hallucination Detection Measure, depending on its detection method.
- It can range from being a Real-Time Model Hallucination Detection Measure to being an Offline Model Hallucination Detection Measure, depending on its detection timing.
- It can range from being a Black-Box Model Hallucination Detection Measure to being a White-Box Model Hallucination Detection Measure, depending on its model access.
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- It can support Model Safety Assessment through reliability measurement.
- It can enable Model Output Filtering via factuality scoring.
- It can facilitate Model Improvement through error pattern identification.
- It can guide Model Deployment Decisions via risk quantification.
- It can inform Model Prompt Engineering through hallucination pattern analysis.
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- Example(s):
- Entailment-Based Model Hallucination Detection Measures, such as:
- FactCC Model Score using textual entailment for model consistency checking.
- DAE Model Score employing dependency arc entailment for model factual verification.
- SummaC Model Metric leveraging NLI models for summary model faithfulness.
- FEQA Model Score using question-answering entailment for model fact checking.
- QA-Based Model Hallucination Detection Measures, such as:
- QAGS Model Score generating question-answer pairs for model content verification.
- QuestEval Model Metric using question generation for model faithfulness assessment.
- BERTScore-QA Model combining BERT similarity with model QA validation.
- Knowledge-Based Model Hallucination Detection Measures, such as:
- WikiFactCheck Model Score verifying against Wikipedia knowledge.
- KG-BERT Model Metric using knowledge graphs for model fact validation.
- FEVER Model Score employing fact extraction and model verification.
- Statistical Model Hallucination Detection Measures, such as:
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- Entailment-Based Model Hallucination Detection Measures, such as:
- Counter-Example(s):
- System Hallucination Detection Measures, which detect system-level hallucinations rather than model-specific hallucinations.
- Model Fluency Measures, which assess text quality rather than factual accuracy.
- Model Toxicity Detection Measures, which identify harmful content rather than factual errors.
- See: Model Hallucination, Model Factuality Assessment, Model Safety Metric, Model Content Verification, Fact Checking Task, Natural Language Inference, Model Evaluation Method.