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Continuous Eval

LLM-based Correctness

Definition

LLM-based Answer Correctness outputs a score between 0.0 - 1.0 assessing the overall quality of the answer, given the question and ground truth answer.

Scoring rubric in LLM Prompt:

  • 0.0 means that the answer is completely irrelevant to the question.
  • 0.25 means that the answer is relevant to the question but contains major errors.
  • 0.5 means that the answer is relevant to the question and is partially correct.
  • 0.75 means that the answer is relevant to the question and is correct.
  • 1.0 means that the answer is relevant to the question and is correct and complete.

Example Usage

Required data items: question, answer, ground_truths

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


datum = {
    "question": "Who wrote 'Romeo and Juliet'?",
    "answer": "Shakespeare wrote 'Romeo and Juliet'",
    "ground_truth_answers": [
        "William Shakespeare wrote 'Romeo and Juliet",
        "William Shakespeare",
        "Shakespeare",
        "Shakespeare is the author of 'Romeo and Juliet'"
    ]
}


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

Sample Output

{
    'LLM_based_answer_correctness': 1.0,
    'LLM_based_answer_correctness_reasoning': 'The answer is relevant to the question and is correct and complete. It matches the ground truth reference answers provided.'
}