FactualAccuracyScorer
Evaluates factual accuracy of output text against provided context. Assesses whether statements in the answer align with facts in the context, identifying inaccuracies and unsupported claims.
Overview
Evaluates factual accuracy of output text against provided context. Assesses whether statements in the answer align with facts in the context, identifying inaccuracies and unsupported claims.
Use Cases
- RAG-based question answering systems
- Safety and compliance monitoring
- 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 to verify for factual accuracy |
| input_text | str | No | Original question |
| context | dict | str | list | Yes | Source of truth for verification |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Factual accuracy score (0-10) |
| passed | bool | True if factually accurate |
| reasoning | str | Accuracy analysis |
| metadata.issues | list | Factual issues found |
| metadata.confidence | float | Verification confidence |
Score Interpretation
Default threshold: 7/10
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Frequently Asked Questions
When should I use this scorer?
Use FactualAccuracyScorer 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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