Upload prompt template grounding_nli_json.yaml

grounding_nli_json.yaml

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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: {}