ENTERPRISE AI ANALYSIS
Constructing Commonsense Knowledge Graph for Persona Consistency
This paper introduces PersonaKG, a bilingual commonsense knowledge graph, and PersonaCOM, a large-scale dialogue dataset, to address persona consistency in AI. It proposes an R² framework (Recognize-Rewrite) to identify and correct inconsistent responses in conversations. Empirical studies show significant improvements in automatic and manual evaluation metrics.
Executive Impact: Key Performance Indicators
Our analysis reveals the following critical metrics, demonstrating the tangible benefits of implementing PersonaKG and the R² framework.
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Automatic Metrics Improvement
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Manual Evaluation Improvement
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PersonaKG Size (Consistent)
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PersonaCOM Size
Deep Analysis & Enterprise Applications
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Recognize-Rewrite Framework Workflow
The R² framework identifies inconsistent responses and rewrites them for persona consistency.
Recognizing Step
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Detect Inconsistent Profiles
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Rewriting Step
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Generate Consistent Responses
PersonaKG Scale
PersonaKG contains a substantial number of consistent and inconsistent persona profiles, enabling comprehensive knowledge integration.
42,679 Total PersonaKG Profile PairsRecognizing Inconsistent Responses
The recognizing step identifies persona inconsistencies using an attention-enhanced BERT-SPC model and a fine-tuned information extraction model (p-UIE). BERT-SPC achieved 0.875 accuracy in identifying inconsistencies, significantly outperforming UIE's 0.463 in profile extraction due to p-UIE's 0.792 F1-score.
Rewriting Consistent Responses
The rewriting step uses a T5-base model, guided by popular consistent-profile knowledge from PersonaKG. This model rephrases inconsistent responses into consistent ones. T5 achieved 0.859 performance, outperforming BART (0.788) and CPT (0.820).
PersonaKG Construction Process
PersonaKG is built through a human-in-the-loop approach to gather consistent and inconsistent persona profiles.
Collecting Profiles
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Pairing Profiles
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Labeling Consistency
PersonaCOM Dataset Generation
The PersonaCOM dataset is constructed through a three-step process to generate dialogues with persona consistency labels.
Collecting Dialogues
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Generating Persona QR pairs
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Injecting Persona QR pairs
| Dataset | Size | Consistent | Inconsistent |
|---|---|---|---|
| PersonaKG | 42,679 | 30,415 | 12,264 |
| PersonaCOM | 26,084 | 7,825 | 5,530 |
Overall Performance Gain
The R² framework provides significant overall improvements across all metrics.
12.20% Average Automatic Metrics ImprovementEnhanced Persona Consistency
Applying the R² framework with PersonaKG leads to substantial improvements in automatic metrics (average 12.20%) and manual evaluation (average 10.09%) across various dialogue models, validating its effectiveness in commonsense-guided persona consistency.
Real-world Scenario: Inconsistent Response Correction
A chatbot's response 'I'm 29 years old and not married.' is followed by 'Yes.' when asked about having kids. The R² framework identifies this as inconsistent (a 29-year-old unmarried person typically doesn't have kids). It then rewrites the response to 'I do not have kids.', maintaining persona consistency.
Before:
User: How old are you and are you single? Bot: I'm 29 years old and not married. User: Do you have kids? Bot: Yes.
After:
User: How old are you and are you single? Bot: I'm 29 years old and not married. User: Do you have kids? Bot: I do not have kids.
Calculate Your Potential ROI
Estimate the efficiency gains and cost savings your enterprise could realize by implementing persona-consistent AI solutions.
Estimated Annual Savings
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Hours Reclaimed Annually
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Your AI Transformation Roadmap
A structured approach to integrating persona-consistent AI into your enterprise operations.
Phase 1: PersonaKG Integration
Integrate PersonaKG into your existing dialogue system for comprehensive persona knowledge.
Phase 2: R² Framework Deployment
Deploy the Recognize-Rewrite (R²) framework to automatically detect and correct persona inconsistencies.
Phase 3: Continuous Learning & Refinement
Utilize PersonaCOM for continuous model training and refinement, ensuring adaptability and accuracy.
Phase 4: Scalable Persona-Consistent AI
Achieve robust and scalable AI systems capable of maintaining consistent personas across diverse interaction scenarios.
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