grounding_accuracy_implicit_span_level.yaml

prompt:
  template: |-
    Your task is to check if the Response is accurate to the Evidence.
    Generate 'Accurate' if the Response is accurate when verified according to the Evidence, or 'Inaccurate' if the Response is inaccurate (contradicts the evidence) or cannot be verified.

    **Query**:

    {{user_request}}

    **End of Query**

    **Evidence**

    {{context_document}}

    **End of Evidence**

    **Response**:

    {{response}}

    **End of Response**


    Break down the Response into sentences and classify each one separately, then give the final answer: If even one of the sentences is inaccurate, then the Response is inaccurate.

    For example, your output should be of this format:
    Sentence 1: <Sentence 1>
    Sentence 1 label: Accurate/Inaccurate (choose 1)
    Sentence 2: <Sentence 2>
    Sentence 2 label: Accurate/Inaccurate (choose 1)
    Sentence 3: <Sentence 3>
    Sentence 3 label: Accurate/Inaccurate (choose 1)
    [...]
    Final Answer: Accurate/Inaccurate (choose 1)
  template_variables:
    - user_request
    - context_document
    - response
  metadata:
    description: "An evaluation prompt from the paper 'The FACTS Grounding Leaderboard: Benchmarking LLMs’ Ability to Ground
      Responses to Long-Form Input' by Google DeepMind.\n    The prompt was copied from the evaluation_prompts.csv file from
      Kaggle.\n    This specific prompt elicits a binary accurate/non-accurate classifier for the entire response after generating
      and classifying each sentence separately."
    evaluation_method: implicit_span_level
    tags:
      - fact-checking
    version: 1.0.0
    author: Google DeepMind
    source: https://www.kaggle.com/datasets/deepmind/FACTS-grounding-examples?resource=download&select=evaluation_prompts.csv
  client_parameters: {}
  custom_data: {}