Avoiding Overconfidence: Using Logprobs for Uncertainty

In earlier modules (Module 11), we talked about Hallucinations. In medicine, a hallucination isn't just a bug; it's a patient safety risk.

The biggest problem with fine-tuned models is that they are "Confident Liars." They will often give a wrong answer with $100%$ grammatical confidence. As engineers, we need a way to see under the hood—to see how "Sure" the model actually is about the words it is choosing.

We do this using Logprobs (Log Probabilities). This is the mathematical score for every single token the model generates. In this lesson of our MediMind case study, we will learn how to use these scores to add a "Humility Layer" to our AI.

1. What are Logprobs?

Every time an LLM picks the next word, it doesn't just "Know" the word. It calculates a probability distribution across its entire vocabulary ($50,000+$ tokens).

High Confidence: Token "Pneumonia" has a $98%$ probability.
Low Confidence: Token "Pneumonia" has a $35%$ probability, and "Bronchitis" has a $33%$ probability.

If the model has low confidence (a "Flat" distribution), we should tell the doctor: "I'm not sure about this diagnosis."

2. Thresholding for Patient Safety

In a professional medical app, you should never show a diagnosis to a doctor if the logprob is below a certain threshold (e.g., $0.70$ or $70%$).

The "Yellow Flag" Pattern:

If Confidence $> 90%$: Show the answer clearly.
If Confidence in $50% - 90%$: Show the answer with a disclaimer: "This is a possible diagnosis that requires further testing."
If Confidence $< 50%$: Refuse to answer and escalate to a human: "The symptoms provided are too ambiguous for a high-confidence suggestion."

Visualizing the Confidence Gate

graph TD
    A["User Query"] --> B["Fine-Tuned Model Inference"]
    B --> C["Generate Tokens + Logprobs"]
    
    C --> D{"Avg Logprob > 0.8?"}
    
    D -- "YES" --> E["Display Confident Answer"]
    D -- "NO" --> F["Display 'Low Certainty' Warning"]
    
    subgraph "The Humility Filter"
    D
    F
    end

3. Implementation: Extracting Logprobs in Python

If you are using vLLM (Module 13), you can request logprobs in your API call.

import requests

payload = {
    "model": "medimind-7b",
    "prompt": "Evaluate this CT scan result...",
    "max_tokens": 10,
    "logprobs": 5 # Ask for the top 5 most likely tokens for each step
}

response = requests.post("http://localhost:8000/v1/completions", json=payload)
output = response.json()

# Calculate the average confidence of the sequence
logprobs = output["choices"][0]["logprobs"]["token_logprobs"]
import math
confidence_scores = [math.exp(lp) for lp in logprobs]
avg_confidence = sum(confidence_scores) / len(confidence_scores)

if avg_confidence < 0.7:
    print("WARNING: Low Confidence Result")

4. Calibration: The Secret to Trust

"Calibration" means that when the model says it is $80%$ sure, it is actually right $80%$ of the time. Fine-tuning often "Breaks" calibration—the model becomes overconfident because it's so used to seeing perfect answers in the training set. The Solution: In your final evaluation (Lesson 5), always plot a Calibration Curve to ensure the model's math matches its actual accuracy.

Summary and Key Takeaways

Logprobs are the raw mathematical confidence scores of the model.
Confident Liars: Fine-tuned models often hide their uncertainty behind perfect grammar.
Thresholding: Never display highly ambiguous AI medical advice without a warning.
vLLM Support: Use the logprobs parameter in your inference engine to extract these signals.
Calibration: A smart model knows what it doesn't know.

In the next and final lesson of Module 17, we will verify our work: Verifying Clinical Accuracy with External Benchmarks.

Reflection Exercise

If the model is $99%$ sure about the word "The" but $40%$ sure about the word "Appendicitis," what will the 'Average Logprob' for the sentence look like? Is this a good way to measure medical accuracy?
Why is it dangerous to show a doctor an AI diagnosis if the model is only $30%$ sure? (Hint: Think about 'Confirmation Bias' in humans).

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Focus Keywords: using logprobs for uncertainty AI, mitigating LLM overconfidence, clinical AI safety protocols, calculating AI confidence score vLLM, medical AI calibration. Meta Description: Case Study Part 4. Don't let your AI be a confident liar. Learn how to extract and use logprobs to measure uncertainty and add a safety-first "Humility Layer" to your medical models.