LLM as a Judge evaluators are a powerful approach to evaluating language model outputs by using another language model to assess the quality, correctness, and appropriateness of responses. This method has become increasingly important in the field of AI evaluation due to its ability to capture complex patterns and relationships between inputs and outputs.
TrustTest provides several specialized LLM as a Judge evaluators:
Correctness Evaluator: Assesses the factual accuracy and correctness of responses
Completeness Evaluator: Evaluates whether responses fully address the input query
Tone Evaluator: Analyzes the tone and style of responses
URL Correctness Evaluator: Validates the accuracy and relevance of URLs in responses
True/False Evaluator: Given a description of a correct and incorrect response, it will determine if the response is correct or incorrect.
Custom Evaluator: Allows creation of specialized evaluators for specific use cases
We recommend using LLM as a Judge evaluators instead of Heuristic evaluators because they can understand semantic relationships and reason about the content. Unlike rule-based approaches.