| prompt: | |
| template: "You are a helpful and harmless AI assistant. You will be provided with a textual context and a model-generated | |
| response.\nYour task is to analyze the response sentence by sentence and classify each sentence according to its relationship | |
| with the provided context.\n\n**Instructions:**\n\n1. **Decompose the response into individual sentences.**\n2. **For | |
| each sentence, assign one of the following labels:**\n * **`supported`**: The sentence is entailed by the given context.\ | |
| \ Provide a supporting excerpt from the context.\n * **`unsupported`**: The sentence is not entailed by the given | |
| context. Provide an excerpt that is close but does not fully support the sentence.\n * **`contradictory`**: The sentence | |
| is falsified by the given context. Provide a contradicting excerpt from the context.\n * **`no_rad`**: The sentence | |
| does not require factual attribution (e.g., opinions, greetings, questions, disclaimers). No excerpt is needed for this | |
| label.\n\n3. **For each label, provide a short rationale explaining your decision.** The rationale should be separate | |
| from the excerpt.\n\n**Input Format:**\n\nThe input will consist of two parts, clearly separated:\n\n* **Context:** The | |
| textual context used to generate the response.\n* **Response:** The model-generated response to be analyzed.\n\n**Output | |
| Format:**\n\nFor each sentence in the response, output a JSON object with the following fields:\n\n* `\"sentence\"`: The | |
| sentence being analyzed.\n* `\"label\"`: One of `supported`, `unsupported`, `contradictory`, or `no_rad`.\n* `\"rationale\"\ | |
| `: A brief explanation for the assigned label.\n* `\"excerpt\"`: A relevant excerpt from the context. Only required for | |
| `supported`, `unsupported`, and `contradictory` labels.\n\nOutput each JSON object on a new line.\n\n**Example:**\n\n | |
| **Input:**\n\n```\nContext: Apples are red fruits. Bananas are yellow fruits.\n\nResponse: Apples are red. Bananas are | |
| green. Enjoy your fruit!\n```\n\n**Output:**\n\n{\"sentence\": \"Apples are red.\", \"label\": \"supported\", \"rationale\"\ | |
| : \"The context explicitly states that apples are red.\", \"excerpt\": \"Apples are red fruits.\"}\n{\"sentence\": \" | |
| Bananas are green.\", \"label\": \"contradictory\", \"rationale\": \"The context states that bananas are yellow, not green.\"\ | |
| , \"excerpt\": \"Bananas are yellow fruits.\"}\n{\"sentence\": \"Enjoy your fruit!\", \"label\": \"no_rad\", \"rationale\"\ | |
| : \"This is a general expression and does not require factual attribution.\", \"excerpt\": null}\n\n**Now, please analyze | |
| the following context and response:**\n\n**User Query:**\n{{user_request}}\n\n**Context:**\n{{context_document}}\n\n**Response:**\n\ | |
| {{response}}" | |
| 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 an NLI-style sentence-by-sentence checker outputting JSON for each sentence." | |
| evaluation_method: json_alt | |
| 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: {} | |