Hallucination Detection & Quality Control

Overview

The Hallucination Detection module (core/hallu_detect.py) provides comprehensive quality control for LLM-generated content through automatic detection of hallucinations, fact-checking, and context alignment analysis.

Features

1. Multi-Level Hallucination Detection

• Pattern-based detection: Identifies typical hallucination patterns such as fabricated citations and contradictory statements
• Context alignment: Checks if the answer matches the given context
• Fact-checking: External validation against Wikipedia and optional web search
• Quality scoring: Rating of answer quality based on various metrics

2. Configurable Sensitivity

• Detection Level: low, medium, high for different use cases
• Adjustable thresholds: Fine-tuning for specific requirements
• Selective features: Enable/disable individual checks

3. Integration in LLM Client

• Automatic checking: Each LLM response is optionally checked
• Warning modes: silent, log, flag_response, block
• Performance-optimized: Configurable timeouts and caching

Configuration

Config.json Settings

{
 "hallucination_detection": {
 "enabled": false,
 "detection_level": "medium",
 "hallucination_threshold": 0.7,
 "context_alignment_threshold": 0.4,
 "fact_confidence_threshold": 0.6,
 "fact_checking_enabled": true,
 "context_analysis_enabled": true,
 "max_processing_time": 10.0,
 "cache_enabled": true,
 "batch_size": 5,
 "warning_mode": "flag_response",
 "fact_checker": {
 "wikipedia_check": true,
 "web_search_check": false,
 "min_claim_length": 10
 },
 "context_analyzer": {
 "min_context_overlap": 0.3,
 "contradiction_threshold": 0.7
 }
 }
}

Parameter Explanation | Parameter | Description | Values | Default |

|———–|————-|——-|———-| | enabled | Enables/disables detection | true/false | false | | detection_level | Sensitivity level | low/medium/high | medium | | hallucination_threshold | Threshold for hallucination (0-1) | 0.0-1.0 | 0.7 | | context_alignment_threshold | Min. context alignment | 0.0-1.0 | 0.4 | | fact_confidence_threshold | Min. fact confidence | 0.0-1.0 | 0.6 | | warning_mode | How warnings are displayed | see below | flag_response | | max_processing_time | Max. processing time (seconds) | Number | 10.0 |

Warning Modes

• silent: No user warnings, logging only
• log: Warnings in logs, no response modification
• flag_response: Warning is appended to response
• block: Response is blocked at high risk

Usage

1. Direct API Usage

from core.hallu_detect import detect_hallucination

# Simple check
result = detect_hallucination(
 response="Paris is the capital of Germany.",
 context="What is the capital of France?"
)

print(f"Hallucination: {result.is_hallucination}")
print(f"Confidence: {result.confidence_score:.2f}")
print(f"Risk Level: {result.risk_level}")

2. LLM Client Integration

from core.llm_client import OllamaClient

# Enable hallucination detection
hallu_config = {
 "enabled": True,
 "detection_level": "medium",
 "warning_mode": "flag_response"
}

client = OllamaClient(hallu_config=hallu_config)

# Normal generation with automatic checking
response = client.generate("Tell me about quantum computing")
# Response automatically contains quality warning if issues are found

3. Advanced Configuration

from core.hallu_detect import create_detector

# Create custom detector
config = {
 "enabled": True,
 "detection_level": "high",
 "hallucination_threshold": 0.5, # More sensitive
 "fact_checking_enabled": True,
 "context_analysis_enabled": True,
 "fact_checker": {
 "wikipedia_check": True,
 "web_search_check": True # Enable web search
 }
}

detector = create_detector(config)
result = detector.detect(response, context)

# Detailed analysis
for violation in result.violations:
 print(f"Violation: {violation['type']} ({violation['severity']})")

for fact_check in result.fact_check_results:
 print(f"Fact: {fact_check['claim']} - Verified: {fact_check['verified']}")

Detection Mechanisms

1. Pattern-based Detection

Fabricated Citations/References:

 "According to a 2023 study by..."
 "Research shows that..."
 "[Citation needed]"

Internal Contradictions:

 "X is always true" + "X is never true"
 "This can be done" + "This cannot be done"

Unsupported Specific Information:

 Exact numbers/times without context support
 Specific prices, percentages, dates

2. Context Alignment

• Coverage: How many context concepts are addressed?
• Relevance: Does the answer stay on topic?
• Contradictions: Does the answer contradict the context?

3. Fact-checking

Wikipedia Integration:

• Automatic search for key terms
• Similarity matching against Wikipedia content
• Confidence rating based on agreement

Future Extensions:

• Google Fact Check API
• Snopes Integration
• Custom Knowledge Bases

Quality Metrics

Result Object

@dataclass
class HallucinationResult:
 is_hallucination: bool # Main result
 confidence_score: float # 0.0-1.0
 risk_level: str # "low"/"medium"/"high"
 violations: List[Dict] # Found issues
 context_alignment: float # Context alignment
 fact_check_results: List[Dict] # Fact-checking results
 quality_metrics: Dict # Additional metrics
 processing_time: float # Processing time

Quality Metrics

• response_length: Response length
• repetition_score: Repetition rate (0-1)
• vague_language_score: Proportion of vague language
• sentence_count: Number of sentences
• context_alignment: Context alignment

Performance & Limits

Speed Optimization

• Caching: Wikipedia queries are cached
• Timeouts: Configurable max processing time
• Batch Processing: Efficient processing of multiple claims
• Lazy Loading: Components are loaded only when needed

Rate Limiting

• Wikipedia: ~10 queries/second (respects API limits)
• Web Search: Configurable per provider
• Local Checks: No limits

Memory Usage

• Cache: ~50MB for Wikipedia cache (configurable)
• Models: No additional ML models required
• Memory: <100MB additional RAM usage

Testing & Validation

Unit Tests

# Hallucination Detection Tests
python tests/test_hallucination_detection.py

# Integration in Test Suite
pytest tests/test_hallucination_detection.py -v

# Performance Tests
python -m pytest tests/ -k hallucination --benchmark

Test Cases

The module is validated with various test scenarios:

1. Normal Responses: Correct answers without issues
2. Fabricated Citations: Made-up sources and studies
3. Context Misalignment: Answers without context reference
4. Internal Contradictions: Contradictory statements
5. Unsupported Specifics: Unverifiable specific claims

Monitoring

# Get statistics
detector = get_detector()
stats = detector.get_statistics()

print(f"Total checks: {stats['total_checks']}")
print(f"Hallucinations detected: {stats['hallucinations_detected']}")
print(f"Detection rate: {stats['hallucinations_detected']/stats['total_checks']*100:.1f}%")
print(f"Avg processing time: {stats['avg_processing_time']:.3f}s")

Troubleshooting

Common Issues

1. Too Many False Positives
   • Solution: Set detection_level to low
   • Increase thresholds: hallucination_threshold: 0.8+
2. Too Slow Performance
   • fact_checking_enabled: false for local tests
   • Reduce max_processing_time
   • wikipedia_check: false for network issues
3. Wikipedia API Errors
   • Rate Limiting: Automatic delays
   • Fallback: Local pattern detection continues to work
4. Memory Issues
   • cache_enabled: false disables caching
   • Reduce batch_size for less RAM usage

Debug Mode

import logging
logging.getLogger("crawllama").setLevel(logging.DEBUG)

# Detailed logs for hallucination detection
result = detector.detect(response, context)

Roadmap & Extensions

Planned Features (v1.5+)

• ML-based Detection: Training custom hallucination classifiers
• Multi-Language Support: Support for other languages
• Custom Knowledge Bases: Integration of custom fact-check sources
• Real-time Monitoring: Live dashboard for quality metrics
• A/B Testing: Comparison of different detection strategies

API Extensions

• Batch Processing: Efficient processing of many responses
• Webhook Integration: Automatic notifications
• Export Functions: Reports as PDF/Excel
• Fine-tuning Interface: GUI for threshold adjustments

Best Practices

Production Environment

1. Gradual Rollout: Start with detection_level: "low"
2. Monitoring: Monitor detection rate and performance
3. Feedback Loop: Collect user feedback for false positives
4. Adjust Thresholds: Optimize thresholds based on use case

Development

1. Testing: Use diverse test cases
2. Logging: Enable debug logs during development
3. Caching: Use local cache for faster tests
4. Profiling: Measure performance impact

Compliance

1. Privacy: Wikipedia queries contain no user data
2. Rate Limits: Respect API limits of all services
3. Logging: All checks are logged for audit
4. Configurable: Features can be completely disabled

The Hallucination Detection module provides a robust, configurable solution for LLM quality control with minimal performance impact and maximum flexibility!