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Continuous Eval

LLM-based Answer Relevance

Definition

LLM-based Answer Relevance outputs a score between 0.0 - 1.0 assessing the consistency of the generated answer based on the reference ground truth answers.

Scoring rubric in LLM Prompt:

  • 0.0 means that the answer is completely irrelevant to the question.
  • 0.5 means that the answer is partially relevant to the question or it only partially answers the question.
  • 1.0 means that the answer is relevant to the question and completely answers the question.

Example Usage

Required data items: question, answer

from continuous_eval.metrics.generation.text import LLMBasedAnswerRelevance
from continuous_eval.llm_factory import LLMFactory


datum = {
    "question": "Who wrote 'Romeo and Juliet'?",
    "answer": "Shakespeare wrote 'Romeo and Juliet'",
}


metric = LLMBasedAnswerRelevance(LLMFactory("gpt-4-1106-preview"))
print(metric(**datum))

Sample Output

{
    'LLM_based_answer_relevance': 1.0,
    'LLM_based_answer_relevance_reasoning': "The answer is relevant to the question and completely answers the question by correctly identifying Shakespeare as the author of 'Romeo and Juliet'."
}