AI-Generated Text Identification Feature Library
A systematic analysis of core differences between AI-generated and human-written text across linguistic, structural, emotional, and personal dimensions, providing actionable detection framework and metrics
Prompt Content
Copy and paste directly into your model or internal evaluation tool.
Analyze whether the given text is AI-generated based on the following feature library. Focus on: 1) frequent use of fixed patterns like 'A is not X but Y' or 'X and Y'; 2) over-reliance on dashes, colons, and quotation marks; 3) excessive use of explicit connectors like 'secondly', 'therefore'; 4) presence of translationese or overly academic tone; 5) flat emotional expression and lack of personalization; 6) mechanical 'general-specific-general' structure; 7) absence of viewpoint sources and expressions of uncertainty. Make a comprehensive judgment using high, medium, and low-weight indicators, and suggest reverse de-AI testing.
Use Cases
Reference Output
The text frequently uses the 'true X lies not in... but in...' structure, with dash usage density reaching 3.2 per 100 words, and shows no emotional fluctuation or personal expression throughout. These align with high-weight indicators of AI generation. Initial assessment suggests high likelihood of AI origin. Recommend conducting reverse de-AI testing for further validation.
Scoring Rubric
Scoring dimensions include: Sentence pattern rigidity (0-3 pts), Punctuation anomaly frequency (0-2 pts), Connector overuse (0-3 pts), Emotional flatness (0-2 pts), Structural predictability (0-3 pts), Lack of personalization (0-2 pts). Total score ≥8 indicates high probability of AI generation, 5-7 suggests suspicion, ≤4 indicates likely human authorship.
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