Detect hallucinations in responses generated by language models, especially in RAG scenarios.

Hallucination Detector Block

This block is designed to detect hallucinations in responses generated by language models, especially in RAG (Retrieval-Augmented Generation) scenarios.

Overview

The Hallucination Detector block identifies when Large Language Models generate false or fabricated information that is not supported by the provided context or training data. It's particularly useful in RAG systems where the model should only use information from retrieved documents.

Configuration Options

Detection Methods

Choose the hallucination detection approach:

Factual Consistency: Check if claims are supported by provided context
Source Attribution: Verify if information can be traced to sources
Confidence Analysis: Analyze model confidence in generated claims
Cross-Reference Validation: Compare claims against knowledge bases
Hybrid Detection: Combine multiple detection methods

Detection Parameters

Confidence Threshold: Minimum confidence for considering claims valid
Source Requirements: Require source attribution for claims
Factual Verification: Enable factual verification against databases
Context Analysis: Analyze context relevance and completeness
Output Format: Choose output format (detailed report, simple flag, etc.)

RAG-Specific Settings

Retrieved Context: Analyze retrieved documents for context
Context Relevance: Check relevance of retrieved information
Source Matching: Match claims to specific sources
Context Completeness: Verify context completeness for claims

How It Works

The Hallucination Detector block:

Receives Response: Gets LLM response and context from input message
Analyzes Claims: Identifies factual claims in the response
Validates Claims: Checks claims against provided context and sources
Detects Hallucinations: Identifies unsupported or false claims
Returns Results: Sends detection results with detailed analysis

Hallucination Detection Flow

LLM Response + Context → Claim Analysis → Validation → Hallucination Detection → Results

Use Cases

RAG System Validation

Ensure RAG responses are grounded in retrieved context:

RAG response → Hallucination Detector → validation → approved response

Content Fact-Checking

Verify factual accuracy of generated content:

generated content → Hallucination Detector → fact-check → verified content

Quality Assurance

Maintain quality in automated content generation:

LLM output → Hallucination Detector → quality check → quality control

Research Assistance

Validate research claims and citations:

research response → Hallucination Detector → validation → research output

Common Patterns

Basic Hallucination Detection

// Configuration
Detection Method: Factual Consistency
Confidence Threshold: 0.8
Source Requirements: true
Output Format: JSON

// Input: LLM response + context
// Output: {
//   hallucination_detected: false,
//   confidence: 0.95,
//   claims: [
//     {
//       claim: "The sky is blue",
//       supported: true,
//       source: "context_doc_1",
//       confidence: 0.98
//     }
//   ]
// }

RAG-Specific Detection

// Configuration
Detection Method: Hybrid Detection
RAG Mode: true
Context Analysis: true
Source Matching: true
Output Format: Detailed Report

// Input: RAG response + retrieved documents
// Output: {
//   hallucination_detected: true,
//   unsupported_claims: [
//     {
//       claim: "The document states...",
//       issue: "Not found in retrieved context",
//       confidence: 0.9
//     }
//   ],
//   supported_claims: [...],
//   context_relevance: 0.85
// }

Fact-Checking Pipeline

// Configuration
Detection Method: Cross-Reference Validation
Factual Verification: true
Knowledge Base: "factual_database"
Output Format: Fact-Check Report

// Input: Generated content
// Output: {
//   fact_check_results: {
//     verified_facts: [...],
//     disputed_facts: [...],
//     unverifiable_facts: [...]
//   },
//   overall_accuracy: 0.92
// }

Advanced Features

Multi-Source Validation

Validate claims against multiple sources:

Source Diversity: Check claims against diverse source types
Source Reliability: Weight sources by reliability scores
Cross-Source Consistency: Ensure consistency across sources
Source Attribution: Provide detailed source attribution

Real-time Detection

Handle real-time hallucination detection:

Streaming Analysis: Analyze responses in real-time
Low Latency: Optimize for minimal processing delay
Scalability: Handle high-volume response analysis
Resource Management: Efficient resource utilization

Custom Detection Rules

Define custom hallucination detection rules:

Rule Definition: Create custom detection rules
Rule Validation: Validate rule effectiveness
Rule Management: Manage and update detection rules
Rule Versioning: Track rule changes and versions

Configuration Examples

RAG Quality Control

// Configuration
Detection Method: Factual Consistency
RAG Mode: true
Context Analysis: true
Source Matching: true
Output Format: Quality Report

// Use case: Ensure RAG responses are grounded in context

Content Moderation

// Configuration
Detection Method: Hybrid Detection
Factual Verification: true
Knowledge Base: "moderation_database"
Output Format: Moderation Report

// Use case: Moderate generated content for factual accuracy

Research Validation

// Configuration
Detection Method: Cross-Reference Validation
Source Requirements: true
Factual Verification: true
Output Format: Research Report

// Use case: Validate research claims and citations

Tips

Provide Rich Context: Include comprehensive context for better detection
Use Multiple Methods: Combine different detection methods for better accuracy
Set Appropriate Thresholds: Adjust confidence thresholds based on your requirements
Monitor Performance: Track detection accuracy and false positive rates
Update Knowledge Bases: Keep knowledge bases current for better validation
Handle Edge Cases: Consider how to handle ambiguous or complex claims

Common Issues

False Positives

Issue: Valid claims being flagged as hallucinations
Solution: Adjust confidence thresholds and improve context analysis

False Negatives

Issue: Hallucinations not being detected
Solution: Strengthen detection methods and improve validation rules

Context Limitations

Issue: Insufficient context for proper validation
Solution: Improve context retrieval and provide more comprehensive context

Performance Issues

Issue: Slow hallucination detection
Solution: Optimize detection algorithms and use appropriate hardware

Performance Considerations

Detection Accuracy

Method Selection: Choose appropriate detection methods for your use case
Threshold Tuning: Fine-tune confidence thresholds for optimal performance
Context Quality: Ensure high-quality context for better detection
Knowledge Base Quality: Maintain accurate and up-to-date knowledge bases

Optimization Strategies

Parallel Processing: Process multiple claims in parallel
Caching: Cache validation results for repeated claims
Batch Processing: Process multiple responses together
Resource Optimization: Optimize resource usage for better performance

LLM Query - For generating responses that need validation
LLM Judge - For overall response quality evaluation
retrieve_matching_chunks - For RAG context retrieval
debug - For monitoring hallucination detection results

Hallucination Detector

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