ClaimVerificationScorer
Extracts individual factual claims from output text, then verifies each claim against context. Provides detailed verification results including supporting/contradicting evidence and confidence scores.
Overview
Extracts individual factual claims from output text, then verifies each claim against context. Provides detailed verification results including supporting/contradicting evidence and confidence scores.
Use Cases
- RAG-based question answering systems
- Hallucination detection in generated content
How It Works
This scorer uses LLM-as-Judge technology to evaluate responses. It prompts a large language model with specific evaluation criteria and the content to assess, then analyzes the LLM's judgment to produce a score and detailed reasoning.
Input Schema
| Parameter | Type | Required | Description |
|---|---|---|---|
| output_text | str | Yes | Answer containing claims to verify |
| input_text | str | No | Original question |
| context | dict | str | list | Yes | Evidence for claim verification |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Claim verification score (0-10) |
| passed | bool | True if claims verified |
| reasoning | str | Verification details |
| metadata.verified_claims | int | Number of verified claims |
| metadata.total_claims | int | Total claims extracted |
Score Interpretation
Default threshold: 7/10
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Frequently Asked Questions
When should I use this scorer?
Use ClaimVerificationScorer when you need to evaluate hallucination and rag aspects of your AI outputs. It's particularly useful for rag-based question answering systems.
Why doesn't this scorer need expected output?
This scorer evaluates quality aspects that don't require comparison against a reference answer. It uses the system prompt and context as the implicit ground truth.
Can I customize the threshold?
Yes, the default threshold of 7 can be customized when configuring the scorer.
Quick Info
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