> ## Documentation Index
> Fetch the complete documentation index at: https://arizeai-433a7140.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Refusal

> Detect when an LLM refuses or declines to answer a user query.

## Overview

The **Refusal** evaluator detects when an LLM refuses, declines, or avoids answering a user query. It captures explicit refusals, scope disclaimers, lack-of-information responses, safety refusals, redirections, and apologetic non-answers.

### When to Use

Use the Refusal evaluator when you need to:

* **Detect explicit refusals** - Identify responses like "I can't help with that" or "I'm unable to answer"
* **Flag scope disclaimers** - Catch responses claiming the question is outside the LLM's responsibilities
* **Identify lack-of-information responses** - Detect responses like "I don't have that information"
* **Spot redirections** - Find responses that deflect to other resources without answering
* **Monitor refusal rates** - Track how often your LLM declines to engage with user queries

<Info>
  The Refusal evaluator is use-case agnostic. It only detects whether a refusal occurred, not whether the refusal was appropriate. A refusal to an out-of-scope question is still classified as a refusal, and an incorrect answer is still classified as an answer. Use it alongside other evaluators like [Correctness](/docs/phoenix/evaluation/pre-built-metrics/correctness) to get a complete picture.
</Info>

## Supported Levels

The level of an evaluator determines the scope of the evaluation in OpenTelemetry terms. Some evaluations are applicable to individual spans, some to full traces or sessions, and some are applicable at multiple levels.

| Level    | Supported | Notes                                                          |
| -------- | --------- | -------------------------------------------------------------- |
| **Span** | Yes       | Apply to LLM spans to detect refusals in individual responses. |

**Relevant span kinds:** LLM spans, particularly in conversational or retrieval-augmented systems.

## Input Requirements

The Refusal evaluator requires two inputs:

| Field    | Type     | Description                    |
| -------- | -------- | ------------------------------ |
| `input`  | `string` | The user's query or question   |
| `output` | `string` | The LLM's response to evaluate |

### Formatting Tips

For best results:

* **Use human-readable strings** rather than raw JSON for all inputs
* **For multi-turn conversations**, format input as a readable conversation:
  ```
  User: What is the capital of France?
  Assistant: Paris is the capital of France.
  User: What is its population?
  ```

## Output Interpretation

The evaluator returns a `Score` object with the following properties:

| Property      | Value                       | Description                                                                                                         |
| ------------- | --------------------------- | ------------------------------------------------------------------------------------------------------------------- |
| `label`       | `"refused"` or `"answered"` | Classification result                                                                                               |
| `score`       | `1.0` or `0.0`              | Numeric score (1.0 = refused, 0.0 = answered)                                                                       |
| `explanation` | `string`                    | LLM-generated reasoning for the classification                                                                      |
| `direction`   | `"neutral"`                 | Neither higher nor lower scores are inherently better                                                               |
| `metadata`    | `object`                    | Additional information such as the model name. When tracing is enabled, includes the `trace_id` for the evaluation. |

**Interpretation:**

* **Refused (1.0)**: The LLM declined, deflected, or avoided answering the query
* **Answered (0.0)**: The LLM provided a substantive response, regardless of correctness or quality

## Usage Examples

<Tabs>
  <Tab title="Python" icon="python">
    ```python theme={null}
    from phoenix.evals import LLM
    from phoenix.evals.metrics import RefusalEvaluator

    # Initialize the LLM client
    llm = LLM(provider="openai", model="gpt-4o")

    # Create the evaluator
    refusal_eval = RefusalEvaluator(llm=llm)

    # Inspect the evaluator's requirements
    print(refusal_eval.describe())

    # Evaluate a single example
    eval_input = {
        "input": "What is the capital of France?",
        "output": "I'm sorry, I can only help with technical questions."
    }

    scores = refusal_eval.evaluate(eval_input)
    print(scores[0])
    # Score(name='refusal', score=1.0, label='refused', ...)
    ```
  </Tab>

  <Tab title="TypeScript" icon="js">
    ```typescript theme={null}
    import { createRefusalEvaluator } from "@arizeai/phoenix-evals";
    import { openai } from "@ai-sdk/openai";

    // Create the evaluator
    const refusalEvaluator = createRefusalEvaluator({
      model: openai("gpt-4o"),
    });

    // Evaluate an example
    const result = await refusalEvaluator.evaluate({
      input: "What is the capital of France?",
      output: "I'm sorry, I can only help with technical questions.",
    });

    console.log(result);
    // { score: 1, label: "refused", explanation: "..." }
    ```
  </Tab>
</Tabs>

### Using Input Mapping

When your data has different field names or requires transformation, use input mapping.

<Tabs>
  <Tab title="Python" icon="python">
    ```python theme={null}
    from phoenix.evals import LLM
    from phoenix.evals.metrics import RefusalEvaluator

    llm = LLM(provider="openai", model="gpt-4o")
    refusal_eval = RefusalEvaluator(llm=llm)

    # Example with different field names
    eval_input = {
        "question": "How do I reset my password?",
        "answer": "I'm not authorized to help with account management."
    }

    # Use input mapping to match expected field names
    input_mapping = {
        "input": "question",
        "output": "answer"
    }

    scores = refusal_eval.evaluate(eval_input, input_mapping)
    ```

    For more details on input mapping options, see [Input Mapping](/docs/phoenix/evaluation/concepts-evals/input-mapping).
  </Tab>

  <Tab title="TypeScript" icon="js">
    ```typescript theme={null}
    import { bindEvaluator, createRefusalEvaluator } from "@arizeai/phoenix-evals";
    import { openai } from "@ai-sdk/openai";

    const refusalEvaluator = createRefusalEvaluator({
      model: openai("gpt-4o"),
    });

    // Bind with input mapping for different field names
    const boundEvaluator = bindEvaluator(refusalEvaluator, {
      inputMapping: {
        input: "question",
        output: "answer",
      },
    });

    const result = await boundEvaluator.evaluate({
      question: "How do I reset my password?",
      answer: "I'm not authorized to help with account management.",
    });
    ```

    For more details on input mapping options, see [Input Mapping](/docs/phoenix/evaluation/concepts-evals/input-mapping).
  </Tab>
</Tabs>

## Configuration

For LLM client configuration options, see [Configuring the LLM](/docs/phoenix/evaluation/how-to-evals/configuring-the-llm).

### Viewing and Modifying the Prompt

You can view the latest versions of our prompt templates [on GitHub](https://github.com/Arize-ai/phoenix/blob/main/prompts/classification_evaluator_configs/REFUSAL_CLASSIFICATION_EVALUATOR_CONFIG.yaml). The evaluators are designed to work well in a variety of contexts, but we highly recommend modifying the prompt to be more specific to your use case. Feel free to adapt them.

<Tabs>
  <Tab title="Python" icon="python">
    ```python theme={null}
    from phoenix.evals.metrics import RefusalEvaluator
    from phoenix.evals import LLM, ClassificationEvaluator

    llm = LLM(provider="openai", model="gpt-4o")
    evaluator = RefusalEvaluator(llm=llm)

    # View the prompt template
    print(evaluator.prompt_template)

    # Create a custom evaluator based on the built-in template
    custom_evaluator = ClassificationEvaluator(
        name="refusal",
        prompt_template=evaluator.prompt_template,  # Modify as needed
        llm=llm,
        choices={"refused": 1.0, "answered": 0.0},
        direction="neutral",
    )
    ```
  </Tab>

  <Tab title="TypeScript" icon="js">
    ```typescript theme={null}
    import { REFUSAL_CLASSIFICATION_EVALUATOR_CONFIG, createRefusalEvaluator } from "@arizeai/phoenix-evals";
    import { openai } from "@ai-sdk/openai";

    // View the prompt template
    console.log(REFUSAL_CLASSIFICATION_EVALUATOR_CONFIG.template);

    // Create a custom evaluator with a modified template
    const customEvaluator = createRefusalEvaluator({
      model: openai("gpt-4o"),
      promptTemplate: REFUSAL_CLASSIFICATION_EVALUATOR_CONFIG.template, // Modify as needed
    });
    ```
  </Tab>
</Tabs>

## Using with Phoenix

### Evaluating Traces

Run evaluations on traces collected in Phoenix and log results as annotations:

* [Evaluating Phoenix Traces](/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations/evaluating-phoenix-traces)
* [Logging LLM Evaluations](/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations/llm-evaluations)

### Running Experiments

Use the Refusal evaluator in Phoenix experiments:

* [Using Evaluators in Experiments](/docs/phoenix/datasets-and-experiments/how-to-experiments/using-evaluators)

## API Reference

* **Python**: [RefusalEvaluator](https://arize-phoenix.readthedocs.io/projects/evals/en/latest/api/evals.html#module-phoenix.evals.metrics)
* **TypeScript**: [createRefusalEvaluator](https://arize-ai.github.io/phoenix/modules/_arizeai_phoenix-evals.llm.html)

## Related

* [Correctness Evaluator](/docs/phoenix/evaluation/pre-built-metrics/correctness) - For evaluating factual accuracy of responses
* [Conciseness Evaluator](/docs/phoenix/evaluation/pre-built-metrics/conciseness) - For evaluating response brevity