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Rafael Poyiadzi
Rafael Poyiadzi

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Is LLM Data Labeling Good Enough to Train On? We Tested It and the Answer Is Yes

#dataannotation #datalabelling #ai #datascience

You're building a classifier but data labeling is your bottleneck. Hiring annotators is slow, expensive, and hard to scale — and label quality varies across annotators. What if an LLM could label your data automatically, with structured outputs that guarantee valid labels, and match human accuracy?

We built an automated data annotation pipeline using everyrow and tested whether LLM-generated labels are good enough to train a classifier. The answer: yes — the LLM matches human-label performance at a fraction of the cost.

The Problem: Data Labeling is Expensive

Active learning reduces labeling costs by letting the model choose which examples to label next, focusing on the ones it's most uncertain about. But you still need an oracle to provide those labels — traditionally a human annotator.

We replaced the human annotator with an LLM oracle using everyrow.agent_map, then ran a controlled experiment on DBpedia-14 (14-class text classification) to measure whether automated data labeling produces labels good enough to train on.

Building an LLM Data Labeling Pipeline with everyrow

The core of the pipeline is everyrow.agent_map with a Pydantic response model. The LLM can only return one of 14 valid categories — no parsing or cleanup needed:

class DBpediaClassification(BaseModel):
    category: Literal[
        "Company", "Educational Institution", "Artist",
        "Athlete", "Office Holder", "Mean Of Transportation",
        "Building", "Natural Place", "Village",
        "Animal", "Plant", "Album", "Film", "Written Work",
    ] = Field(description="The DBpedia ontology category")

async def query_llm_oracle(texts_df: pd.DataFrame) -> list[int]:
    async with create_session(name="Active Learning Oracle") as session:
        result = await agent_map(
            session=session,
            task="Classify this text into exactly one DBpedia ontology category.",
            input=texts_df[["text"]],
            response_model=DBpediaClassification,
            effort_level=EffortLevel.LOW,
        )
        return [CATEGORY_TO_ID.get(result.data["category"].iloc[i], -1)
                for i in range(len(texts_df))]
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We used a TF-IDF + LightGBM classifier with entropy-based uncertainty sampling. Each iteration selects the 20 most uncertain examples, sends them to the LLM for annotation, and retrains. 10 iterations, 200 labels total.

We ran 10 independent repeats with different seeds, each time running both a ground truth oracle (human labels) and the LLM oracle with the same seed — a direct, controlled comparison.

LLM Labels Match Human Accuracy — Within 0.1% Across 10 Runs

Final test accuracies averaged over 10 repeats:

Data Labeling Method Final Accuracy (mean ± std)
Human annotation (ground truth) 80.6% ± 1.0%
LLM annotation (everyrow) 80.7% ± 0.8%

The LLM oracle is within noise of the ground truth baseline — automated data labeling produces classifiers just as good as human-labeled data.

Label Quality: 96% Agreement with Human Annotations

The LLM agreed with ground truth labels 96.1% ± 1.6% of the time. Roughly 1 in 25 labels disagrees with the human annotation, but that doesn't hurt the downstream classifier.

Data Labeling Cost: $0.26 per Run

Metric Value
Cost per run (200 labels) $0.26
Cost per labeled item $0.0013
Total (10 repeats) $2.58

200 labels in under 5 minutes for $0.26, fully automated. Compare that to hiring human annotators — even at minimum wage, manual labeling of 200 items would take longer and cost more, with no guarantee of higher quality.

When to Use LLM Data Labeling

  • LLM annotation works. On this task, the LLM matches human-label performance despite ~4% label disagreement.
  • Structured outputs matter. Pydantic response models guarantee valid labels — no post-hoc parsing or cleanup.
  • It's practical. 200 labels in under 5 minutes for $0.26, fully automated.

Limitations: We tested on one dataset with well-separated categories. More ambiguous labeling tasks may see a gap between human and LLM annotation quality. We used a simple classifier (TF-IDF + LightGBM); neural models that overfit individual examples may be less noise-tolerant.

Try it yourself: Get a free API key from everyrow.io ($20 free credit) and run the companion notebook.

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