Accuracy ScorerLLM-as-Judge

AccuracyScorer

Calculates accuracy based on substring or token-level matching between prediction and ground truth. More lenient than ExactMatchScorer, allowing for partial matches.

Overview

Calculates accuracy based on substring or token-level matching between prediction and ground truth. More lenient than ExactMatchScorer, allowing for partial matches.

accuracyrule-basedbenchmarkpartial-matchflexible

Use Cases

  • Accuracy benchmarking and validation

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

ParameterTypeRequiredDescription
predictionstrYesGenerated output
ground_truthstrYesExpected output

Output Schema

FieldTypeDescription
scorefloatPartial match score (0-10)
passedboolTrue if above threshold
reasoningstrMatch analysis
metadatadictToken overlap details

Score Interpretation

Default threshold: 7/10

9-10ExcellentResponse fully meets all evaluation criteria
7-8GoodResponse meets most criteria with minor issues
5-6FairResponse partially meets criteria, needs improvement
3-4PoorResponse has significant issues
0-2FailingResponse fails to meet basic criteria

Frequently Asked Questions

When should I use this scorer?

Use AccuracyScorer when you need to evaluate accuracy and rule-based aspects of your AI outputs. It's particularly useful for accuracy benchmarking and validation.

Why does this scorer need expected output?

This scorer compares the generated output against a known expected result to calculate accuracy metrics.

Can I customize the threshold?

Yes, the default threshold of 7 can be customized when configuring the scorer.

Quick Info

CategoryAccuracy
Evaluation TypeLLM-as-Judge
Requires Expected OutputYes
Default Threshold7/10

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