Self-evaluation in AI refers to a model's ability to assess, critique, and score its own outputs. This metacognitive capability enables language models to identify errors, rate confidence, and improve responses through self-reflection, without requiring external feedback.

Common Self-Evaluation Approaches

• Self-Scoring: Ask the model to rate its own response on a scale (e.g., "Rate the accuracy of your answer from 1-10 and explain why").
• Self-Verification: Generate a response, then prompt the model to check it for factual errors, logical inconsistencies, or missing information.
• Self-Critique: Ask the model to identify weaknesses in its own output and suggest improvements.
• Consistency Checking: Generate multiple responses and check whether they agree — inconsistency signals potential errors.

Applications

• Constitutional AI: Anthropic's approach uses self-critique against a set of principles to improve safety without human labels.
• Self-Refine: Generate → critique → revise loop that iteratively improves response quality.
• Confidence Estimation: The model's self-assessed confidence can flag responses that need human review.
• Best-of-N Selection: Generate N responses, have the model score each, and return the highest-rated one.

Limitations

• Overconfidence: Models are often poorly calibrated — they may rate incorrect answers highly because they "sound right."
• Blind Spots: A model that makes an error due to a knowledge gap will also fail to detect that error in self-evaluation.
• Sycophantic Self-Assessment: Models tend to rate their own outputs favorably, especially when the evaluation prompt doesn't explicitly encourage criticism.
• Inconsistency: Self-evaluation scores can vary significantly across runs or phrasings.

When It Works Well

Self-evaluation is most reliable for detecting format errors, logical contradictions, and internally inconsistent claims. It is least reliable for factual accuracy verification, where the model may confidently confirm its own hallucinations.