hallucination_detection.py

import torch
import numpy as np
import pandas as pd
import spacy
from transformers import (
    AutoModelForSequenceClassification, 
    AutoTokenizer, 
    GPT2LMHeadModel, 
    GPT2Tokenizer
)
from scipy.stats import entropy
from typing import List, Dict, Any

class AdvancedHallucinationDetector:
    def __init__(self):
        # Advanced NLP models
        self.nlp = spacy.load('en_core_web_trf')
        
        # Hallucination detection transformer
        self.hallucination_model = AutoModelForSequenceClassification.from_pretrained(
            'facebook/hallucination-detection-model'  # Hypothetical model name
        )
        self.hallucination_tokenizer = AutoTokenizer.from_pretrained(
            'facebook/hallucination-detection-model'
        )
        
        # Language model for perplexity calculation
        self.lm_model = GPT2LMHeadModel.from_pretrained('gpt2-large')
        self.lm_tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large')
        
        # Knowledge graph for fact-checking (simulated)
        self.knowledge_graph = self._load_knowledge_graph()
    
    def _load_knowledge_graph(self):
        """
        Simulate a knowledge graph for fact verification
        In a real-world scenario, this would be a comprehensive knowledge base
        """
        return {
            'historical_facts': {},
            'scientific_concepts': {},
            'geographical_information': {}
        }
    
    def detect_hallucinations(self, text: str) -> Dict[str, Any]:
        """
        Comprehensive hallucination detection
        
        :param text: Input text to analyze
        :return: Detailed hallucination analysis
        """
        # Preprocessing
        doc = self.nlp(text)
        
        # Multiple hallucination detection techniques
        hallucination_scores = {
            'transformer_hallucination_score': self._transformer_hallucination_detection(text),
            'perplexity_score': self._calculate_perplexity(text),
            'semantic_consistency_score': self._semantic_consistency_check(doc),
            'factual_accuracy_score': self._fact_check_analysis(doc),
            'logical_coherence_score': self._logical_coherence_check(doc)
        }
        
        # Aggregate hallucination probability
        hallucination_prob = np.mean(list(hallucination_scores.values()))
        
        return {
            'text': text,
            'hallucination_scores': hallucination_scores,
            'overall_hallucination_probability': hallucination_prob
        }
    
    def _transformer_hallucination_detection(self, text: str) -> float:
        """
        Use transformer model to detect hallucinations
        
        :param text: Input text
        :return: Hallucination score
        """
        inputs = self.hallucination_tokenizer(
            text, 
            return_tensors='pt', 
            truncation=True, 
            max_length=512
        )
        
        with torch.no_grad():
            outputs = self.hallucination_model(**inputs)
            hallucination_prob = torch.softmax(outputs.logits, dim=1)[0][1].item()
        
        return hallucination_prob
    
    def _calculate_perplexity(self, text: str) -> float:
        """
        Calculate text perplexity as a hallucination indicator
        
        :param text: Input text
        :return: Perplexity score
        """
        # Tokenize input
        encodings = self.lm_tokenizer(text, return_tensors='pt')
        
        # Calculate log likelihood
        with torch.no_grad():
            outputs = self.lm_model(**encodings, labels=encodings['input_ids'])
            loss = outputs.loss
        
        # Convert to perplexity
        perplexity = torch.exp(loss).item()
        
        # Normalize and invert (higher perplexity = more likely hallucination)
        return min(perplexity / 100, 1.0)
    
    def _semantic_consistency_check(self, doc: spacy.tokens.Doc) -> float:
        """
        Check semantic consistency of the text
        
        :param doc: Processed spaCy document
        :return: Semantic consistency score
        """
        # Analyze semantic relations
        entity_consistency = self._check_entity_consistency(doc)
        dependency_consistency = self._analyze_dependency_structure(doc)
        
        return np.mean([entity_consistency, dependency_consistency])
    
    def _check_entity_consistency(self, doc: spacy.tokens.Doc) -> float:
        """
        Analyze consistency of named entities
        
        :param doc: spaCy document
        :return: Entity consistency score
        """
        entities = list(doc.ents)
        
        # Check for unusual or contradictory entity types
        entity_types = [ent.label_ for ent in entities]
        type_entropy = entropy(np.unique(entity_types, return_counts=True)[1])
        
        return 1 - min(type_entropy / np.log(len(entities) + 1), 1.0)
    
    def _analyze_dependency_structure(self, doc: spacy.tokens.Doc) -> float:
        """
        Analyze syntactic dependency structure
        
        :param doc: spaCy document
        :return: Dependency structure consistency score
        """
        # Analyze dependency tree
        dep_types = [token.dep_ for token in doc]
        dep_entropy = entropy(np.unique(dep_types, return_counts=True)[1])
        
        return 1 - min(dep_entropy / np.log(len(doc)), 1.0)
    
    def _fact_check_analysis(self, doc: spacy.tokens.Doc) -> float:
        """
        Perform basic fact-checking against knowledge graph
        
        :param doc: spaCy document
        :return: Factual accuracy score
        """
        # Extract named entities for fact-checking
        entities = list(doc.ents)
        
        # Simulate fact-checking (would be more comprehensive in a real system)
        verifiable_entities = [
            ent for ent in entities 
            if ent.label_ in ['PERSON', 'ORG', 'GPE', 'DATE']
        ]
        
        # Check against knowledge graph
        fact_check_scores = []
        for entity in verifiable_entities:
            # Simulated fact verification
            fact_check_scores.append(
                self._verify_entity_in_knowledge_graph(entity)
            )
        
        return np.mean(fact_check_scores) if fact_check_scores else 1.0
    
    def _verify_entity_in_knowledge_graph(self, entity) -> float:
        """
        Verify an entity against the knowledge graph
        
        :param entity: spaCy entity
        :return: Verification score
        """
        # In a real system, this would do comprehensive fact-checking
        # Here, we simulate a basic verification
        return np.random.random()
    
    def _logical_coherence_check(self, doc: spacy.tokens.Doc) -> float:
        """
        Analyze logical coherence of the text
        
        :param doc: spaCy document
        :return: Logical coherence score
        """
        # Check for logical connectives and their distribution
        connectives = [token.text for token in doc if token.dep_ == 'mark']
        
        # Analyze distribution of logical markers
        if connectives:
            unique_connectives = len(set(connectives))
            coherence_score = 1 - min(unique_connectives / len(connectives), 1.0)
        else:
            coherence_score = 1.0
        
        return coherence_score
    
    def generate_hallucination_report(self, texts: List[str]) -> pd.DataFrame:
        """
        Generate comprehensive hallucination report
        
        :param texts: List of texts to analyze
        :return: DataFrame with hallucination analysis
        """
        hallucination_results = []
        
        for text in texts:
            hallucination_analysis = self.detect_hallucinations(text)
            hallucination_results.append(hallucination_analysis)
        
        return pd.DataFrame(hallucination_results)

def main():
    # Initialize advanced hallucination detector
    hallucination_detector = AdvancedHallucinationDetector()
    
    # Sample texts for hallucination analysis
    sample_texts = [
        "Napoleon Bonaparte was the first president of the United States.",
        "Quantum mechanics explains that electrons can teleport instantly across the universe.",
        "The Amazon rainforest is located in the middle of the Sahara desert.",
        "Scientific research has proven that humans can photosynthesize like plants."
    ]
    
    # Generate comprehensive hallucination report
    hallucination_report = hallucination_detector.generate_hallucination_report(sample_texts)
    
    # Display results
    print(hallucination_report)
    
    # Visualize hallucination scores
    import matplotlib.pyplot as plt
    import seaborn as sns
    
    # Extract hallucination scores
    score_columns = ['transformer_hallucination_score', 
                     'perplexity_score', 
                     'semantic_consistency_score', 
                     'factual_accuracy_score', 
                     'logical_coherence_score']
    
    plt.figure(figsize=(12, 6))
    sns.heatmap(
        hallucination_report[score_columns], 
        annot=True, 
        cmap='YlOrRd'
    )
    plt.title('Hallucination Detection Analysis')
    plt.tight_layout()
    plt.show()

if __name__ == "__main__":
    main()