Model-Assisted Data Preprocessing for Better Fine-Tuning

Improving Training Data Quality by Using Another Model

If you’re planning to train a model, one of the easiest wins is improving the quality of the data you train on—and it often doesn’t take much effort. A practical approach is to have another model process your dataset first, so the final training data is cleaner, more consistent, and more informative.

Below are a few ways this kind of preprocessing and augmentation can help.

Standardize the Data Before You Train

Sometimes “improving quality” is as simple as making the dataset more uniform. For example:

• Ensuring samples are a relatively consistent size (when that’s helpful for your training setup)
• Normalizing formatting so data follows similar conventions across the dataset

These changes can reduce noise and make it easier for your model to learn patterns instead of learning quirks in how the data was assembled.

Augment Data by Adding Structured Analysis

You can also improve training data by enriching it—adding extra signal that helps the model learn things you care about.

For example, if you want a model to understand nuances in human speech or develop awareness of writing styles, you don’t have to provide only raw writing samples and then separately explain what’s happening. Instead, you can include the original writing sample and then include it again with a breakdown.

That breakdown might call attention to what’s happening in each paragraph and pull out specific features you want the model to notice. This is a form of data augmentation: you’re not just giving more data, you’re giving more explicit structure.

Example: Distinguish Facts vs. Opinions

A concrete example is taking a newspaper article and labeling different types of statements. You might:

• Identify which parts are statements of fact
• Identify which parts are opinions
• Mark them explicitly (for example, in a tagged/tokenized way) so the model can learn the difference

This kind of annotation helps a model learn distinctions that may otherwise be implicit or inconsistently expressed.

Add Missing Context and References

Another useful augmentation is resolving references so each training example contains the context needed to understand it.

For instance, if you have smaller sections of text that refer to information elsewhere, you can include those references directly. A common case is pronouns: if a paragraph uses “him” or “her” but doesn’t include the person’s name, you can build a system that revisits each article or section and ensures the relevant identifiers are present.

This can make examples more self-contained and reduce ambiguity—especially important when training a model to follow entities and context across text.

Takeaway

Using one model to preprocess, standardize, and enrich your training data can be a low-effort way to get a higher-quality dataset. Whether you’re normalizing formatting, adding analytical breakdowns, tagging facts vs. opinions, or filling in missing context, the goal is the same: make the signal clearer so the model learns what you actually intend.