artifact-detection

Testing & Quality

Detect annotation artifacts and shortcuts in benchmarks

QUICK START

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Source SKILL.md: https://github.com/yogsoth-ai/de-anthropocentric-research-engine/blob/HEAD/skills/artifact-detection/SKILL.md

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Artifact Detection Tactic

Systematically probe benchmarks for annotation artifacts, dataset shortcuts, and spurious correlations that allow models to achieve high scores without the intended capability.

Stages

Stage 1: Hypothesis-Only Baseline Test

Search literature for evidence that partial-input baselines achieve unexpectedly high performance:

Hypothesis-only baselines (NLI without premise)
Question-only baselines (QA without context)
Label-word frequency baselines
Majority-class and surface-pattern baselines

Search queries: "[benchmark] annotation artifacts", "[benchmark] hypothesis only", "[benchmark] spurious correlations", "[benchmark] dataset bias"

If published partial-input results exist, record performance gap between partial and full input. Gap < 10 points above random indicates severe artifacts.

Stage 2: Contrast Set Construction

Identify whether contrast sets or adversarial evaluations exist:

Search for "[benchmark] contrast sets", "[benchmark] adversarial examples"
Check if CheckList-style behavioral tests have been applied
Look for counterfactual data augmentation studies

Record performance drops on contrast sets. Drops > 20 points indicate reliance on surface patterns.

Stage 3: Format Manipulation Probes

Search for evidence of format sensitivity:

Prompt template sensitivity studies
Label name/ordering effects
Verbalization effects in classification
Input length correlations with labels

Record whether minor format changes cause disproportionate score changes.

Stage 4: Conclusion Synthesis

Aggregate evidence into artifact severity assessment:

Severity	Criteria
Critical	Partial-input baseline within 5 points of full model
High	Contrast set drop >20 points OR format sensitivity >10 points
Medium	Known artifacts documented but partial mitigations exist
Low	Minor artifacts, full-input still required for high performance
None	No evidence of artifacts (may indicate insufficient probing)

Output

artifact_report:
  benchmark: string
  overall_severity: critical|high|medium|low|none
  partial_input_baselines:
    - input_type: string  # e.g., "hypothesis only"
      performance: float
      full_model_performance: float
      gap: float
      source: string
  contrast_set_results:
    - contrast_set: string
      original_performance: float
      contrast_performance: float
      drop: float
      source: string
  format_sensitivity:
    - manipulation: string
      score_range: string
      source: string
  shortcuts_identified:
    - shortcut: string
      mechanism: string
      exploitability: high|medium|low
  evidence_completeness: thorough|partial|minimal

Yield Report

Metric	Minimum
Literature sources checked	5
Artifact categories probed	3
Evidence items collected	4
Severity classification produced	1