Why Your Fine-Tuned Model Still Hallucinates - and What to Do About It

So you've fine-tuned your language model. Maybe it answers in your tone of voice. Maybe it knows your domain. But then it starts saying things that aren’t true. Things you never taught it.

This is hallucination, and it's not just a problem in pretraining. It’s also a data quality problem during fine-tuning.

In this article, we’ll unpack why hallucinations persist even after fine-tuning - and how Datricity AI helps prevent them by addressing the hidden flaws in your training data.

What Is a Hallucination in AI?

A hallucination occurs when a language model generates an output that is:

❌ Factually incorrect
❌ Internally inconsistent
❌ Not supported by the prompt or data

Fine-tuned models hallucinate when they’re trained on noisy, contradictory, or low-quality data - or when the structure of the prompt-completion pairs leads to confusion.

Why Hallucinations Happen After Fine-Tuning

Fine-tuning is often thought of as a fix-all - just show the model your data and it will behave. But if your dataset has any of the following, hallucination can get worse:

🧹 Noisy inputs

Poor grammar, incomplete answers, irrelevant fields

🔁 Duplicates and contradictions

Repetitive or semantically similar prompts with inconsistent completions

🧩 Poor labeling and structure

Prompts that don’t match the completion or switch intent halfway

🧠 Unclear instruction types

Mixing Q&A, summarization, classification, and dialog formats without separation

When you fine-tune on this kind of data, the model doesn’t learn clarity - it learns confusion.

Example: Contradictory Fine-Tuning

Consider a dataset with these two examples:

{"prompt": "What is your return policy?", "completion": " Returns are accepted within 30 days."}
{"prompt": "What is your return policy?", "completion": " We do not accept returns under any circumstances."}

A model trained on both may confidently assert either, or neither - because it learned conflicting information.

How Datricity AI Helps Prevent Hallucination

Datricity AI is built to catch the silent errors that cause models to hallucinate. Here's how:

✅ 1. Clean and Normalize Text

Remove layout noise, HTML fragments, junk tokens
Standardize punctuation, capitalization, and spacing

✅ 2. Semantic Deduplication

Detect and eliminate near-duplicate entries that cause training drift
Group conflicting examples and let you select the canonical version

✅ 3. Prompt-Completion Alignment

Automatically analyze whether completions match prompts
Flag examples where intent is mismatched or vague

✅ 4. Format Consistency

Separate mixed task types (e.g., instruction-tuning vs. Q&A)
Apply consistent structure to all examples

Results: More Truthful, Targeted Responses

Customers using Datricity AI for cleaning and deduplication have seen:

📉 Up to 80% reduction in hallucinated answers
📈 Increased consistency across completions
✅ Fewer user-reported errors in production

When your training data is clean, aligned, and trustworthy, your model can be too.

The Bottom Line

Fine-tuning isn’t magic. It's surgical instruction - and bad instructions lead to bad outcomes.

If your model is hallucinating, the problem isn’t always the architecture. It’s often your dataset.

With Datricity AI, you can identify and remove the root causes of hallucination before they get baked into your model.