Why Your Model is Hallucinating: The Truth Gap

"Hallucination"—the act of a model confidently stating a falsehood—is the #1 fear of AI developers. You fine-tune a model to be a "Medical Expert," but it suggests a treatment that doesn't exist. You fine-tune it to be a "Legal Assistant," but it cites a fake court case.

When your fine-tuned model starts lying, you have two possible suspects: Your Data or Your Hyperparameters.

In this lesson, we will learn how to distinguish between a model that "doesn't know" and a model that "is being forced to guess."

1. Data-Driven Hallucinations (The Garbage-In Effect)

The most common cause of hallucinations in fine-tuning is Noise in the Golden Dataset.

Scenario: The Conflicting Teacher

In Example 1, you say the capital of California is Sacramento.
In Example 50, you accidentally say the capital is San Francisco (maybe the synthetic data teacher like GPT-4o made a mistake).
The Result: The model's weights are pulled in two directions. During inference, it might "Mix" the two and say "Sacra-Francisco."

Scenario: The Knowledge Gap

Fine-tuning is excellent for learning Formatting, but poor for learning New Facts. If your training data contains facts that were not in the model's original "Brain" (Pretraining), the model will struggle to reconcile them. It will try to "Map" the new fact onto its old concepts, creating a hallucination.

2. Hyperparameter-Driven Hallucinations (The Confident Guesser)

Sometimes the model knows the right answer, but your settings are forcing it to act erratically.

suspect A: High Temperature (Inference)

The Temperature setting controls the randomness. If you set temperature to 1.0+, the model is encouraged to pick less-likely tokens. For a specialized medical or legal bot, this is catastrophic.

Fix: For fine-tuned specialized models, set temperature to 0.1 or 0.0 (Deterministic).

Suspect B: Low Rank ($r$) with High Learning Rate

If your LoRA rank is too small (e.g., $r=4$) but your learning rate is high, the model "Compresses" too much information into too few parameters. This creates "Collisions" where two different concepts start using the same weights, leading the model to confuse them.

Visualizing the Hallucination Root

graph TD
    A["Hallucination Observed"] --> B{"Is the model consistently wrong?"}
    
    B -- "Yes (Same wrong answer every time)" --> C["Suspect: Training Data (Check for noise/errors)"]
    B -- "No (Different wrong answer every time)" --> D["Suspect: Inference Temperature (Lower to 0.0)"]
    
    C --> E["Action: Clean high-signal examples"]
    D --> F["Action: Use Greedy Decoding"]
    
    subgraph "Logic Trace"
    C
    D
    end

3. The "Instruction Follower" Hallucination

Sometimes a model hallucinates because it is Too Obedient. If you fine-tune a model to "Always provide a solution," and then you ask it a question that has no solution, it will invent one rather than saying "I don't know."

The fix: You must include "Negative Samples" in your training data (Module 5). You need examples where the user asks a nonsense question and the assistant response is: "I'm sorry, I cannot answer that based on the provided documentation."

Implementation: Testing for Hallucinations in Python

To debug a lying model, we use a "Repeatability Test."

def hallucination_test(prompt, model, tokenizer):
    # We run the same prompt 10 times with a medium temperature
    responses = []
    for _ in range(10):
        output = generate_response(prompt, model, tokenizer, temperature=0.7)
        responses.append(output)
    
    # Check for consistency
    unique_answers = set(responses)
    if len(unique_answers) > 1:
        print(f"[ALERT] Model is unstable. Varied answers: {len(unique_answers)}")
    else:
        print("[SUCCESS] Model is consistent (Stable).")

# If the model is consistent but WRONG, the data is the problem.
# If the model is inconsistent, the temperature/generation settings are the problem.

Summary and Key Takeaways

Data Consistency: A single wrong example in your training set can "Poison" the model's accuracy.
Knowledge Base: Don't use fine-tuning to teach facts that RAG could handle better.
Negative Samples: Train the model to say "I don't know" to prevent forced hallucinations.
Inference Tuning: Lower your temperature for specialized tasks to ensure deterministic truth.

In the next lesson, we will look at technical errors: Fixing Formatting and Syntax Errors.

Reflection Exercise

If your model correctly cites a law but gets the "Clause Number" wrong, is that a failure of behavior or a failure of facts?
Why is "Greedy Decoding" (Temperature 0) the best way to determine if your training data is correct? (Hint: Does the model have any 'Randomness' to hide behind when temperature is zero?)

SEO Metadata & Keywords

Focus Keywords: LLM hallucinations fine-tuning, training data noise AI, greedy decoding vs temperature, negative sampling fine-tuning, fixing ai model lies. Meta Description: Stop the lies. Learn how to trace hallucinations back to their source—whether it's noisy training data, missing negative samples, or incorrect inference settings.

Why Your Model is Hallucinating (Data vs. Hyperparameters)