LLM Annotation Reliability Gap

The systematic difference between LLM annotation performance on benchmarks versus real-world annotation tasks. LLMs often appear to match or exceed human annotators on standard tasks but fail in subtle ways on novel or domain-specific annotation.

The Gap

Benchmark Performance: LLMs achieve high agreement with human labels on established datasets Production Reality: Performance degrades on:

• Domain-specific terminology
• Edge cases not in training data
• Tasks requiring world knowledge cutoff
• Subjective judgments requiring calibration

Why It Happens

1. Training Data Overlap: Benchmarks may be in training data
2. Distribution Shift: Real annotation tasks differ from benchmark distributions
3. No Calibration: LLMs can’t be “trained” on your specific annotation guidelines
4. Confidence Without Competence: Uniform confidence masks uncertainty

Implications

• Don’t assume benchmark performance transfers
• Always validate on held-out examples from your specific task
• Consider LLM annotation as first pass, not final label
• Monitor for drift over time

Related: 05-atom—uniform-confidence-problem, 03-molecule—annotation-task-suitability-framework