ConsistAI

⚠️ Important Note: This is a theoretical framework that proposes novel evaluation methods for knowledge editing. It has not been empirically tested and is presented as a conceptual contribution building on existing research. The implementation serves as a starting point for future experimental validation.

Features

Core Evaluation Components

• Chain-of-thought verification system
• Semantic drift tracking
• Knowledge graph infrastructure
• Temporal consistency validation
• Edit rejection analysis

Built on Research

• Evaluation methodology from "HalluEditBench" (Huang et al., 2024)
• Memory editing concepts from "Mass-Editing Memory in a Transformer" (Meng et al., 2023)
• Model editing techniques from "Locating and Editing Factual Associations in GPT" (Meng et al., 2022)

Installation

pip install -r requirements.txt

Usage

from editing_framework import EditEvaluator, EditInstance
from transformers import AutoModelForCausalLM, AutoTokenizer

# Initialize framework
model = AutoModelForCausalLM.from_pretrained("model_name")
tokenizer = AutoTokenizer.from_pretrained("model_name")
evaluator = EditEvaluator(model, tokenizer)

# Create edit instance
edit = EditInstance(
    id="edit_001",
    subject="OpenAI",
    relation="CEO",
    original_object="Sam Altman",
    target_object="Emmett Shear",
    domain="technology",
    topic="companies"
)

# Evaluate edit
results = evaluator.evaluate_edit(edit)

Components

ChainOfThoughtVerifier

• Validates edits using reasoning templates
• Generates verification queries
• Calculates confidence scores

verification = thought_verifier.verify_edit(edit)

SemanticDriftTracker

• Measures semantic changes post-edit
• Tracks neighborhood effects
• Provides drift severity metrics

drift_metrics = drift_tracker.track_drift(edit)

KnowledgeGraph

• Maintains fact relationships
• Enforces temporal consistency
• Checks for conflicting edits

consistency = knowledge_graph.check_consistency(entity)

Evaluation Metrics

The framework evaluates edits across multiple dimensions:

Consistency

• Temporal consistency: Tracks changes over time
• Relational consistency: Validates relationship logic
• Graph-based validation: Ensures knowledge graph coherence

Semantic Analysis

• Cosine drift: Measures embedding space changes
• Neighborhood effects: Analyzes impact on related concepts
• Embedding distance: Quantifies semantic shifts

Verification

• Reasoning chain validity: Evaluates logical consistency
• Confidence scoring: Assesses edit reliability
• Conflict detection: Identifies contradictions

Example Output

{
    'edit_id': 'edit_001',
    'status': 'accepted',
    'consistency': {
        'temporal': [],
        'relational': []
    },
    'semantic_drift': {
        'cosine_drift': 0.82,
        'neighbor_drift': 0.76,
        'drift_severity': 'low'
    },
    'verification': {
        'confidence_score': 0.89,
        'reasoning_chain': [...],
        'potential_conflicts': []
    }
}

Theoretical Foundations

This framework proposes several novel theoretical contributions:

1. Chain-of-thought verification system extends the basic evaluation methods from HalluEditBench by introducing structured reasoning paths.

2. Semantic drift tracking introduces a new theoretical approach to measuring knowledge consistency through embedding space analysis.

3. The knowledge graph infrastructure proposes a theoretical model for tracking temporal and relational consistency of edits.

The actual effectiveness of these methods requires empirical validation through future research.

Requirements

• Python 3.8+
• PyTorch 1.9+
• Transformers 4.20+
• NetworkX 2.6+

Future Research Directions

1. Empirical validation of the proposed metrics
2. Comparison with existing evaluation frameworks
3. Extension to multi-modal knowledge editing
4. Integration with various LLM architectures
5. Development of standardized benchmarks

Contributing

This is a theoretical framework open for academic discussion and improvement. Contributions that enhance the theoretical foundation or propose validation methods are welcome.

Citation

@article{huang2024hallueditbench,
  title={HalluEditBench: Can Knowledge Editing Really Correct Hallucinations?},
  author={Huang, Baixiang and Chen, Canyu and Xu, Xiongxiao and Payani, Ali and Shu, Kai},
  journal={arXiv preprint arXiv:2410.16251},
  year={2024}
}

Disclaimer

This codebase is a theoretical proposal and should not be used in production environments without thorough testing and validation. The components and metrics proposed here are intended to spark discussion and future research in knowledge editing evaluation methods.