CitationQualityScorer
Evaluates quality of source attribution and citations in answers. Assesses accuracy, completeness, specificity, and appropriateness of source references.
Overview
Evaluates quality of source attribution and citations in answers. Assesses accuracy, completeness, specificity, and appropriateness of source references.
Use Cases
- RAG-based question answering systems
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 with citations to evaluate |
| input_text | str | Yes | Original query |
| context | dict | str | list | Yes | Source documents for verification |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Citation quality score (0-10) |
| passed | bool | True if citations are adequate |
| reasoning | str | Citation analysis |
| metadata | dict | Citation verification details |
Score Interpretation
Default threshold: 6/10
Related Scorers
Frequently Asked Questions
When should I use this scorer?
Use CitationQualityScorer when you need to evaluate quality 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 6 can be customized when configuring the scorer.
Quick Info
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