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What is Self-Consistency Evaluation Metric?
A self-consistency evaluation metric measures how often a model arrives at the same answer when sampling multiple reasoning paths or generations for one question. Higher agreement usually signals more reliable reasoning for that prompt.
What this looks like in production
Teams sample N completions (temperature greater than 0), cluster final answers, and score majority agreement - useful for math, planning, and agent tool choice.
What teams usually do about it
- Use alongside gold metrics; consistency != correctness.
- Track self-consistency after prompt or model changes.
- Combine with groundedness for RAG tasks.
Related Giskard articles
- Announcing Giskard OSS v3 for the agentic era
- A practical guide to LLM hallucinations and misinformation
Measure answer stability with Giskard
Add self-consistency-style checks to your eval suite when reasoning paths matter. See Giskard OSS v3 or giskard.ai.
Further reading: arXiv: Self-Consistency Improves Chain of Thought.
