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

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

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

🔁 Duplicates and contradictions

🧩 Poor labeling and structure

🧠 Unclear instruction types

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

✅ 2. Semantic Deduplication

✅ 3. Prompt-Completion Alignment

✅ 4. Format Consistency

Results: More Truthful, Targeted Responses

Customers using Datricity AI for cleaning and deduplication have seen:

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.

Datricity AI
Jun 24, 2025