LLM Score Evaluator

The LLM Score Evaluator is a sophisticated component that assigns numerical scores to language model outputs based on predefined criteria and evaluation metrics. It enables quantitative assessment of response quality and appropriateness.

LLM Score Evaluator interface and configuration

Usage Note: Define clear scoring criteria and evaluation prompts to ensure consistent and meaningful scores. The evaluator's effectiveness depends on well-structured evaluation guidelines.

Component Inputs

Input Text: The text to be evaluated
Example: "User query or context"
Generated Output: The model's response to evaluate
Example: "Model's generated response"
Context(s): Additional context for scoring
Example: "Relevant background information"
Language Model: The LLM to use for evaluation
Example: "gpt-4", "claude-2"
Evaluation Prompt: Custom scoring criteria
Example: "Score based on clarity, accuracy, and relevance"

Component Outputs

Score: Numerical evaluation score
Example: 8.5 out of 10
Explanation: Detailed scoring rationale
Example: "Strong clarity and accuracy, but could improve relevance"
Breakdown: Individual criteria scores
Example: {clarity: 9, accuracy: 8.5, relevance: 8}

How It Works

The LLM Score Evaluator employs a systematic approach to assess and score language model outputs. It uses predefined criteria and rubrics to ensure consistent and objective evaluation.

Evaluation Process

Input analysis and context consideration
Criteria-based assessment
Score calculation per criterion
Overall score computation
Explanation generation
Detailed feedback compilation

Use Cases

Quality Assessment: Score responses based on quality metrics
Performance Monitoring: Track LLM output quality over time
Response Ranking: Compare multiple responses quantitatively
Model Evaluation: Assess model performance across criteria
Quality Control: Maintain consistent output standards

Implementation Example

const scoreEvaluator = new LLMScoreEvaluator({
  inputText: "Explain quantum computing",
  generatedOutput: "Quantum computing leverages quantum mechanics...",
  context: "Technical explanation context",
  languageModel: "gpt-4",
  evaluationPrompt: "Score based on accuracy, clarity, and depth"
});

const result = await scoreEvaluator.evaluate();

// Output:
// {
//   score: 8.5,
//   explanation: "Strong technical accuracy and clarity...",
//   breakdown: {
//     accuracy: 9.0,
//     clarity: 8.5,
//     depth: 8.0
//   }
// }

Additional Resources

Best Practices

Define clear and measurable scoring criteria
Use consistent evaluation prompts
Calibrate scoring across different evaluators
Document scoring rationale thoroughly
Regularly review and update scoring criteria

Documentation