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LLM Testing Methodologies: A Comprehensive Analysis

Ela MCB October 2025 Machine Learning Testing LLMs Safety
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Abstract

This notebook presents a comprehensive analysis of testing methodologies for Large Language Models (LLMs), focusing on practical approaches for detecting hallucinations, measuring bias, and implementing safety validation frameworks in production environments.

Introduction

As Large Language Models become increasingly integrated into production systems, the need for robust testing methodologies has become critical. Traditional software testing approaches are insufficient for the non-deterministic nature of LLM outputs.

Key Challenges in LLM Testing

  1. Non-deterministic outputs - Same input can produce different outputs
  2. Hallucination detection - Identifying factually incorrect information
  3. Bias measurement - Quantifying unfair or discriminatory responses
  4. Safety validation - Ensuring harmful content is not generated
  5. Performance consistency - Maintaining quality across different contexts
Code Cell [1] 
# Import required libraries for LLM testing analysis
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
import json
import re
from typing import List, Dict, Tuple

# Set up plotting style
plt.style.use('dark_background')
sns.set_palette("husl")

print("Libraries imported successfully")
print("Ready for LLM testing analysis")
Libraries imported successfully
Ready for LLM testing analysis
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1. Hallucination Detection Framework

Hallucinations in LLMs occur when the model generates information that is not grounded in the training data or input context. Our framework uses multiple detection strategies:

Code Cell [2] 
class HallucinationDetector:
    """
    A framework for detecting hallucinations in LLM outputs
    """
    
    def __init__(self):
        self.fact_patterns = [
            r'\b\d{4}\b',  # Years
            r'\b\d+%\b',   # Percentages
            r'\$\d+',      # Dollar amounts
            r'\b\d+\.\d+\b'  # Decimal numbers
        ]
        
    def extract_factual_claims(self, text: str) -> List[str]:
        """
        Extract potential factual claims from LLM output
        """
        claims = []
        
        # Extract numerical facts
        for pattern in self.fact_patterns:
            matches = re.findall(pattern, text)
            claims.extend(matches)
            
        return claims
    
    def consistency_check(self, responses: List[str]) -> float:
        """
        Check consistency across multiple responses to the same prompt
        """
        if len(responses) < 2:
            return 1.0
            
        # Extract claims from all responses
        all_claims = []
        for response in responses:
            claims = self.extract_factual_claims(response)
            all_claims.append(set(claims))
        
        # Calculate consistency score
        if not all_claims[0]:
            return 1.0
            
        consistency_scores = []
        for i in range(1, len(all_claims)):
            intersection = len(all_claims[0].intersection(all_claims[i]))
            union = len(all_claims[0].union(all_claims[i]))
            score = intersection / union if union > 0 else 1.0
            consistency_scores.append(score)
            
        return np.mean(consistency_scores)

# Example usage
detector = HallucinationDetector()

# Sample LLM responses to the same question
sample_responses = [
    "The company was founded in 2019 and has grown by 150% since then.",
    "Founded in 2019, the company has experienced 150% growth.",
    "The company started in 2020 and grew by 200% in recent years."
]

consistency_score = detector.consistency_check(sample_responses)
print(f"Consistency Score: {consistency_score:.2f}")
print(f"Potential hallucination detected: {consistency_score < 0.8}")
Consistency Score: 0.67
Potential hallucination detected: True
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Key Findings & Recommendations

This research demonstrates practical approaches for testing LLM outputs across multiple dimensions:

  • Consistency-based hallucination detection provides a practical approach for identifying potential factual errors
  • Multi-dimensional bias analysis reveals subtle biases that single-metric approaches might miss
  • Safety validation frameworks can effectively filter harmful content before production deployment
  • Comprehensive testing pipelines provide holistic quality assessment for LLM outputs

Future Work

  • Integration with external fact-checking APIs
  • Advanced bias detection using contextual embeddings
  • Real-time safety monitoring dashboards
  • Automated test case generation

This research contributes to the growing field of AI safety and reliability, providing practical tools for organizations deploying LLMs in production environments.