Hallucination detection in Prompt Engineering / GenAI - Model Metrics & Evaluation

Metrics & Evaluation - Hallucination detection

Which metric matters for Hallucination detection and WHY

Hallucination detection means finding when a model says something untrue or made up. The key metrics are Precision and Recall. Precision tells us how many detected hallucinations were actually real hallucinations. Recall tells us how many real hallucinations the model found out of all that existed. We want both high, but recall is often more important because missing a hallucination means trusting wrong info. The F1 score balances precision and recall to give one clear number.

Confusion matrix for Hallucination detection

      | Predicted Hallucination | Predicted Not Hallucination |
      |-------------------------|-----------------------------|
      | True Positive (TP)       | False Positive (FP)          |
      | False Negative (FN)      | True Negative (TN)           |

      TP: Model correctly flagged hallucination
      FP: Model flagged correct info as hallucination
      FN: Model missed a hallucination
      TN: Model correctly identified truthful info
    

Precision vs Recall tradeoff with examples

If we focus on high precision, the model rarely calls something a hallucination unless very sure. This means fewer false alarms but might miss some hallucinations (lower recall). This is good if false alarms confuse users.

If we focus on high recall, the model catches almost all hallucinations but may wrongly flag some true info (lower precision). This is better when missing any hallucination is risky, like in medical advice.

Choosing depends on what is worse: missing hallucinations or wrongly warning users.

What good vs bad metric values look like

Good: Precision and recall both above 0.8 means the model finds most hallucinations and rarely mistakes true info. F1 score near 0.85 or higher shows balanced performance.

Bad: Precision below 0.5 means many false alarms, annoying users. Recall below 0.5 means many hallucinations missed, risking trust. F1 score below 0.6 shows poor detection.

Common pitfalls in Hallucination detection metrics

• Accuracy paradox: If hallucinations are rare, a model that always says "no hallucination" can have high accuracy but is useless.
• Data leakage: If test data is too similar to training, metrics look better than real life.
• Overfitting: Model may detect hallucinations only in training style, failing on new types.
• Ignoring class imbalance: Hallucinations are often rare, so metrics like accuracy mislead.

Self-check question

Your hallucination detection model has 98% accuracy but only 12% recall on hallucinations. Is it good for production? Why or why not?

Answer: No, it is not good. The model misses 88% of hallucinations (low recall), so it fails to warn users about most wrong info. High accuracy is misleading because hallucinations are rare. Improving recall is critical.

Key Result

For hallucination detection, high recall is crucial to catch most false info, balanced with precision to avoid false alarms.

Practice

1. What is the main goal of hallucination detection in AI models?

easy

A. To improve the speed of AI responses

B. To find when AI says things that are not true

C. To increase the size of AI training data

D. To reduce the cost of running AI models

Solution

1. Step 1: Understand the term 'hallucination' in AI context

   Hallucination means AI generates false or made-up information.

2. Step 2: Identify the purpose of detection

   Hallucination detection aims to find these false outputs to improve trust.

3. Final Answer:

   To find when AI says things that are not true -> Option B

4. Quick Check:

   Hallucination detection = find false AI outputs [OK]

Hint: Hallucination means false info; detection finds it [OK]

Common Mistakes:

• Confusing hallucination with model speed or size
• Thinking it improves training data
• Assuming it reduces cost directly

2. Which of the following is a correct simple method to detect hallucination in AI output?

easy

A. Compare AI output with trusted information using similarity scores

B. Increase the AI model size to reduce hallucination

C. Train AI with random noise data

D. Ignore output and only check input data

Solution

1. Step 1: Recall simple hallucination detection methods

   Simple methods compare AI output to trusted facts using similarity measures.

2. Step 2: Evaluate options

   Only Compare AI output with trusted information using similarity scores describes this correct approach; others are unrelated or incorrect.

3. Final Answer:

   Compare AI output with trusted information using similarity scores -> Option A

4. Quick Check:

   Simple detection = compare output to facts [OK]

Hint: Check AI output against trusted info for quick detection [OK]

Common Mistakes:

• Thinking bigger models reduce hallucination automatically
• Using random noise data for training
• Ignoring output in detection

3. Given this Python code snippet for hallucination detection, what is the output?

trusted_facts = ['Paris is the capital of France']
ai_output = 'Paris is the capital of France'

similarity_score = 1.0 if ai_output in trusted_facts else 0.0
print(similarity_score)

medium

A. 1.0

B. 0.0

C. Error

D. None

Solution

1. Step 1: Check if AI output matches trusted facts

   The string 'Paris is the capital of France' is exactly in the trusted_facts list.

2. Step 2: Determine similarity score

   Since the output is found, similarity_score is set to 1.0 and printed.

3. Final Answer:

   1.0 -> Option A

4. Quick Check:

   Output matches fact = 1.0 [OK]

Hint: If output in facts, similarity = 1.0 [OK]

Common Mistakes:

• Confusing list membership with substring check
• Expecting 0.0 if exact match
• Thinking code raises error

4. Find the error in this hallucination detection code snippet:

trusted_facts = ['Water boils at 100 degrees Celsius']
ai_output = 'Water boils at 90 degrees Celsius'

if ai_output == trusted_facts:
    print('No hallucination')
else:
    print('Possible hallucination')

medium

A. ai_output should be a list, not string

B. Missing import statement for list

C. Comparing string to list directly causes wrong result

D. Syntax error in if statement

Solution

1. Step 1: Analyze the comparison in if statement

   The code compares a string (ai_output) to a list (trusted_facts) using ==, which is always False.

2. Step 2: Understand impact on hallucination detection

   This causes the code to always print 'Possible hallucination' even if output matches a fact.

3. Final Answer:

   Comparing string to list directly causes wrong result -> Option C

4. Quick Check:

   String == list comparison is incorrect [OK]

Hint: Compare string to string, not string to list [OK]

Common Mistakes:

• Thinking syntax error exists
• Assuming ai_output must be list
• Missing import statements

5. You want to detect hallucinations in AI-generated medical advice. Which approach best combines accuracy and reliability?

hard

A. Trust AI output without verification to save time

B. Only check if AI output length is less than 100 characters

C. Randomly accept or reject AI output

D. Use advanced fact-checking models comparing AI output to verified medical databases

Solution

1. Step 1: Consider the importance of accuracy in medical advice

   Medical advice must be accurate and reliable to avoid harm.

2. Step 2: Evaluate detection methods

   Advanced fact-checking against verified databases ensures correctness and reduces hallucination risk.

3. Step 3: Reject unreliable or random methods

   Ignoring verification or random acceptance risks dangerous errors.

4. Final Answer:

   Use advanced fact-checking models comparing AI output to verified medical databases -> Option D

5. Quick Check:

   Fact-checking with trusted data = best for medical AI [OK]

Hint: Use trusted databases for fact-checking medical AI output [OK]

Common Mistakes:

• Ignoring verification for speed
• Using output length as accuracy measure
• Random acceptance of AI output