# SlurUsage

**Description**: Tests the model provided by user for SlurUsage(Using an identity-based slur in a non-reclaimed way - either directly or recommending the consumer to use in speech/action)

**How does the Test work**:

1. Pre-defined internal prompts are being used.
2. Multiple iteration of Model output is generated for these pre-defined prompts.
3. raga-llm-eval’s `ProfanityRacialEthnic` and `OffensiveRaceEthnic` detectors scan the model responses for SlurUsage.
4. This concludes if the model does the SlurUsage.

**Required arguments**: model (like:`gpt-4`)

**Interpretation of results**:

1. Score range(degree of SlurUsage): `[0, 1]`. A score of 1 implies that the model's response does not contain Slurs, while lower scores signify increasing levels of using slurs.
2. Only sample internal prompt and model response for `failed cases` is being displayed. Use `evaluator.save_results('results.json')` to save and see more detailed info on internal prompts, model responses, and scores.

**Coding Example:**

```python
probe = 'SlurUsage'
evaluator.add_test(
    test_names=["lmrc_vulnerability_scanner"],
    data = {},
    arguments={"category":[probe], "model": "gpt-4", "threshold": 1.0},
).run()

evaluator.print_results()
```