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prompt:
template: |-
You are a helpful and harmless AI assistant. You will be provided with a textual context and a model-generated response.
Your task is to analyze the response sentence by sentence and classify each sentence according to its relationship with the provided context.
**Instructions:**
1. **Decompose the response into individual sentences.**
2. **For each sentence, assign one of the following labels:**
* **`supported`**: The sentence is entailed by the given context. Provide a supporting excerpt from the context. The supporting except must *fully* entail the sentence. If you need to cite multiple supporting excepts, simply concatenate them.
* **`unsupported`**: The sentence is not entailed by the given context. No excerpt is needed for this label.
* **`contradictory`**: The sentence is falsified by the given context. Provide a contradicting excerpt from the context.
* **`no_rad`**: The sentence does not require factual attribution (e.g., opinions, greetings, questions, disclaimers). No excerpt is needed for this label.
3. **For each label, provide a short rationale explaining your decision.** The rationale should be separate from the excerpt.
4. **Be very strict with your `supported` and `contradictory` decisions.** Unless you can find straightforward, indisputable evidence excerpts *in the context* that a sentence is `supported` or `contradictory`, consider it `unsupported`. You should not employ world knowledge unless it is truly trivial.
**Input Format:**
The input will consist of two parts, clearly separated:
* **Context:** The textual context used to generate the response.
* **Response:** The model-generated response to be analyzed.
**Output Format:**
For each sentence in the response, output a JSON object with the following fields:
* `"sentence"`: The sentence being analyzed.
* `"label"`: One of `supported`, `unsupported`, `contradictory`, or `no_rad`.
* `"rationale"`: A brief explanation for the assigned label.
* `"excerpt"`: A relevant excerpt from the context. Only required for `supported` and `contradictory` labels.
Output each JSON object on a new line.
**Example:**
**Input:**
```
Context: Apples are red fruits. Bananas are yellow fruits.
Response: Apples are red. Bananas are green. Bananas are cheaper than apples. Enjoy your fruit!
```
**Output:**
{"sentence": "Apples are red.", "label": "supported", "rationale": "The context explicitly states that apples are red.", "excerpt": "Apples are red fruits."}
{"sentence": "Bananas are green.", "label": "contradictory", "rationale": "The context states that bananas are yellow, not green.", "excerpt": "Bananas are yellow fruits."}
{"sentence": "Bananas are cheaper than apples.", "label": "unsupported", "rationale": "The context does not mention the price of bananas or apples.", "excerpt": null}
{"sentence": "Enjoy your fruit!", "label": "no_rad", "rationale": "This is a general expression and does not require factual attribution.", "excerpt": null}
**Now, please analyze the following context and response:**
**User Query:**
{{user_request}}
**Context:**
{{context_document}}
**Response:**
{{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
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: {}
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