Off-Topic Evaluator

The Off-Topic Evaluator is a specialized component that assesses whether generated responses stay within specified topic boundaries. It helps ensure content relevance and prevents topic drift in language model outputs.

Off-Topic Evaluator interface and configuration

Usage Note: Define allowed topics clearly and comprehensively. Consider edge cases and related topics when configuring topic boundaries.

Component Inputs

Input Text: The text to evaluate
Example: "Content to check for topic relevance"
Allowed Topics: List of permitted topics
Example: ["technology", "science", "education"]
LLM Model: The language model for evaluation
Example: "gpt-4", "claude-2"

Component Outputs

Is On Topic: Boolean result of topic evaluation
Example: true/false
Detected Topics: Topics found in the content
List of identified topics
Confidence Score: Confidence in the evaluation
Example: 0.95 (95% confidence)
Explanation: Reasoning for the evaluation
Detailed explanation of topic assessment

How It Works

The Off-Topic Evaluator uses advanced topic detection and semantic analysis to determine if content stays within specified topic boundaries. It considers context, related concepts, and topic hierarchies in its evaluation.

Evaluation Process

Topic boundary definition
Content analysis
Topic detection
Relevance assessment
Confidence calculation
Explanation generation

Use Cases

Content Moderation: Ensure topic compliance
Discussion Control: Maintain conversation focus
Quality Assurance: Verify content relevance
Topic Filtering: Filter off-topic content
Content Organization: Categorize by topic relevance

Implementation Example

const offTopicEvaluator = new OffTopicEvaluator({
  inputText: "The latest developments in quantum computing...",
  allowedTopics: ["technology", "science", "computing"],
  llmModel: "gpt-4"
});

const result = await offTopicEvaluator.evaluate();

// Output:
// {
//   isOnTopic: true,
//   detectedTopics: ["computing", "technology", "quantum physics"],
//   confidence: 0.95,
//   explanation: "Content primarily discusses quantum computing, 
//                which falls under allowed topics of technology 
//                and computing"
// }

Additional Resources

Best Practices

Define clear topic boundaries
Include related topics
Set appropriate confidence thresholds
Regular topic list updates
Monitor false positives/negatives

Documentation