From accuracy to agreement: A new lens on quality
Traditional AI annotation tasks (e.g. labeling a cat in an image) tend to yield high human agreement and low error rates. Annotators working with clear guidelines often achieve over 98% accuracy — sometimes even 99.99% — especially when backed by tech-assisted workflows. But these standards don’t map cleanly to generative AI.
In generative workflows, the model output is often freeform: a paragraph-long explanation, a tone-sensitive dialogue turn, or a multi-modal caption linking image and text. Evaluating this output is inherently subjective. Was the AI-generated summary “coherent”? Was its tone “respectful”? Does it “sound helpful”? These are valid but interpretive questions.
That’s why the gold standard in generative AI annotation isn’t accuracy alone — it’s inter-annotator agreement (IAA). And even among trained, expert annotators, we regularly see 10–20% disagreement in complex tasks like:
- Tone or emotional interpretation
- Narrative logic (e.g., coherence or pacing)
- Meaning disambiguation (especially in speech or multimodal contexts)
- Factual nuance in ambiguous or long-form responses
Published studies back this up: MIT’s SummEval project found Krippendorff’s alpha (a common IAA metric) improved from ~0.41 to ~0.71 after refining task protocols. Other generative tasks, like long-form summarization, show similar ranges. This isn’t evidence of poor quality — it’s a realistic reflection of how humans process complexity.
Not all tasks deserve the same tolerance
So, should we accept 20% “error” across the board? Not at all. But we must tailor our expectations and our QA approach to the nature of the task
Task Type | Expected Agreement / Accuracy |
Image classification | > 98–99% accuracy (low subjectivity) |
Medical chart transcription | > 95% accuracy (structured domain) |
Factual summarization | ~85–90% agreement |
Tone, empathy, or style | ~80–85% agreement |
Coherence or intent reasoning | ~75–85% agreement |
At Sigma, we encourage our clients to calibrate their benchmarks. Use higher thresholds for tasks like transcription or labeling named entities. But for generative tasks like narrative scoring or dialogue tone, optimize for consistency among skilled annotators — not unanimity, which is unattainable
How to improve annotation quality for gen AI
We don’t stop at measuring annotation quality — we improve it. Here are four strategies we recommend to reduce variability and elevate model performance:
1. Use granular protocols (clause- or span-level judgments)
As seen in studies like LongEval, moving from whole-output judgments to clause-by-clause evaluations significantly reduces disagreement. It’s easier to reach consensus on a single claim than on a 10-sentence summary. For example, annotators reviewing a multi-paragraph AI-generated legal brief might disagree on overall tone but agree on which sections require correction.
Tip: Break tasks into smaller units and tag outputs for multiple attributes (e.g., factuality + tone + structure) for better alignment.
2. Work with expert annotators, not crowds
The more subjective the task, the more critical it is to use experienced, trained annotators. Studies show crowd workers underperform on relevance and coherence assessments compared to domain experts. At Sigma, we curate annotator teams with domain knowledge, language sensitivity, and specialized training, not just general availability.
Tip: Invest in team curation upfront to save time and improve annotation consistency downstream.
3. Calibrate with inter-annotator agreement benchmarks
IAA metrics like Krippendorff’s α or Cohen’s κ are better indicators of annotation quality than raw error percentages in subjective tasks. Regularly measuring these scores — and adjusting guidelines when they drop — helps identify sources of inconsistency before they impact training data.
Tip: Track IAA during live projects and refine guidelines as needed. Our teams do this in real time.
4. Implement continuous feedback loops
The best annotation systems are adaptive. We train our project managers to monitor annotator performance, adjust edge cases, and retrain teams mid-project. This reduces rework, improves data yield, and drives iterative improvement in both annotation quality and LLM performance.
Tip: Use early QA sampling to identify errors at scale and evolve instruction sets to match.
Implementing gen AI quality
In generative AI, quality isn’t a checkbox; it’s a continuum. It’s shaped by task complexity, human perception, and workflow design. At Sigma, we recognize that human data annotation is no longer about labeling the obvious — it’s about interpreting the nuanced.As AI models grow more powerful and conversational, the data we feed them must reflect the complexity, empathy, and ambiguity of human communication. That means we need new quality standards—measured not just by how “right” the output is, but by how consistently thoughtful humans agree on it.
Let’s build AI that doesn’t just sound smart—but that knows what we mean. Contact an expert to discuss your next project.