LLM-Generated Labels for Custom Model Training: A Research Case Study

The Dataset Desert We Encountered

During a research project with a university partner, we hit a wall that's frustratingly common in applied machine learning: the perfect dataset simply didn't exist. Our document enrichment pipeline required fast, accurate multi-label classification to tag documents with meaningful categories for trend analysis and aggregate insights. 

The problem? We needed classification capabilities for a domain where no off-the-shelf models existed, and every available dataset fell short of our specific requirements.

Our Performance Constraints

Speed was non-negotiable. Our pipeline processes documents continuously, and each classification step directly impacts our overall throughput. Using a large language model for inference on every document would create an unacceptable bottleneck. We needed something fast, lightweight, and purpose-built for our exact use case.

The Custom Training Challenge

Training a custom RoBERTa model seemed like the obvious solution, but it created a chicken-and-egg problem: we needed a labeled dataset to train the model, but the datasets we could find were either:

• Too narrow in scope for our multi-label requirements

• Focused on adjacent but not identical classification tasks

• Missing the nuanced categories our downstream analysis required

• Simply too small to train an effective model

The LLM Labeling Solution

Rather than compromise on our requirements or spend months manually labeling thousands of examples, we turned to an underutilized application of large language models: automated dataset creation.

Our Labeling Pipeline

Step 1: Prompt Engineering for Consistency 

We crafted detailed prompts that clearly defined our classification categories, provided examples, and established consistent labeling criteria. The key was making our requirements explicit enough that the LLM could replicate human-level judgment.

Step 2: Batch Processing for Efficiency 

We processed our unlabeled documents through the LLM in batches, generating comprehensive multi-label annotations that matched our exact taxonomy and requirements.

Step 3: Quality Control and Validation 

We implemented validation steps to ensure label quality, including confidence scoring and manual spot-checking of edge cases.

Training the Custom RoBERTa Model

With our LLM-generated dataset in hand, we trained a RoBERTa model specifically for our classification task. The model learned to replicate the LLM's labeling decisions but with dramatically faster inference times, perfect for our high-throughput pipeline requirements.

Real-World Results

The approach delivered exactly what we needed:

• Speed: 

  RoBERTa inference was 50-100x faster than LLM calls for each document

  
  

• Accuracy: 

  The trained model maintained high performance on our specific classification tasks

  
  

• Cost: 

  Eliminated ongoing API costs for document classification

  
  

• Research Success:

  The project achieved its research objectives and produced meaningful insights

Why This Approach Works

LLM-generated labeling represents a paradigm shift in custom model development. Instead of being constrained by existing datasets or expensive manual annotation, we could create exactly the training data we needed. The LLM served as an expert annotator, providing consistent, high-quality labels at scale, while the resulting fine-tuned model gave us the performance characteristics our production system required.

The Broader Implications

This experience highlighted how LLMs excel as dataset generation tools, not just for text generation, but for creating the structured, labeled data that traditional ML models need to thrive. For research projects and specialized applications where perfect datasets don't exist, LLM labeling offers a practical path from concept to production without the traditional bottlenecks of data acquisition and annotation.

The combination of LLM intelligence for data creation and smaller model efficiency for production inference creates a powerful development pattern that we believe is significantly underutilized in the current ML landscape