Row 3308

Row ID: 3308 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 3308 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

The **Azure OpenAI Service** incorporates a content filtering system that works alongside its core models. Here’s how it operates:

1. **Ensemble of Classification Models**: The content filtering system runs both the input prompt and the generated completion through an ensemble of classification models. These models aim to detect and prevent the output of harmful content. They cover four main categories:

* **Hate**: Content that attacks or uses discriminatory language based on attributes like race, ethnicity, nationality, gender identity, sexual orientation, religion, and more. * **Sexual**: Material related to explicit sexual content. * **Violence**: Content involving violence or harm. * **Self-harm**: Material related to self-harm or suicide.

1. **Severity Levels**: The severity levels for filtering are categorized as:

* **Safe**: Content detected at this level is labeled but isn’t subject to filtering and isn’t configurable. * **Low**, **Medium**, and **High**: These levels indicate the severity of harmful content and allow for configurability.

1. **Additional Optional Models**:

* **Jailbreak Risk Detection**: Binary classifiers that flag whether user or model behavior qualifies as a jailbreak attack. * **Known Content Detection**: Flags known text or source code.

1. **Language Support**: The content filtering models are specifically trained and tested in languages such as English, German, Japanese, Spanish, French, Italian, Portuguese, and Chinese. However, the service can work in other languages, but quality may vary. 2. **Monitoring**: The service also monitors for behaviors that might violate product terms. 3. **Application Design Considerations**: Variations in API configurations and application design can affect filtering behavior. [It’s essential to test and ensure suitability for your specific application](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/content-filter)[1](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/content-filter).

Remember that content filtering aims to create a safer environment while maintaining flexibility for developers. If you have any further questions, feel free to ask! 😊

FieldValue
text The **Azure OpenAI Service** incorporates a content filtering system that works alongside its core models. Here’s how it operates: 1. **Ensemble of Classification Models**: The content filtering system runs both the input prompt and the generated completion through an ensemble of classification models. These models aim to detect and prevent the output of harmful content. They cover four main categories: * **Hate**: Content that attacks or uses discriminatory language based on attributes like r…
label r/gpt3
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communityName r/GPT3
datetime 2024-02-27
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Raw Record

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  "text": " \n\nThe **Azure OpenAI Service** incorporates a content filtering system that works alongside its core models. Here’s how it operates:\n\n1. **Ensemble of Classification Models**: The content filtering system runs both the input prompt and the generated completion through an ensemble of classification models. These models aim to detect and prevent the output of harmful content. They cover four main categories:\n\n* **Hate**: Content that attacks or uses discriminatory language based on attributes like race, ethnicity, nationality, gender identity, sexual orientation, religion, and more.\n* **Sexual**: Material related to explicit sexual content.\n* **Violence**: Content involving violence or harm.\n* **Self-harm**: Material related to self-harm or suicide.\n\n1. **Severity Levels**: The severity levels for filtering are categorized as:\n\n* **Safe**: Content detected at this level is labeled but isn’t subject to filtering and isn’t configurable.\n* **Low**, **Medium**, and **High**: These levels indicate the severity of harmful content and allow for configurability.\n\n1. **Additional Optional Models**:\n\n* **Jailbreak Risk Detection**: Binary classifiers that flag whether user or model behavior qualifies as a jailbreak attack.\n* **Known Content Detection**: Flags known text or source code.\n\n1. **Language Support**: The content filtering models are specifically trained and tested in languages such as English, German, Japanese, Spanish, French, Italian, Portuguese, and Chinese. However, the service can work in other languages, but quality may vary.\n2. **Monitoring**: The service also monitors for behaviors that might violate product terms.\n3. **Application Design Considerations**: Variations in API configurations and application design can affect filtering behavior. [It’s essential to test and ensure suitability for your specific application](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/content-filter)[1](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/content-filter).\n\nRemember that content filtering aims to create a safer environment while maintaining flexibility for developers. If you have any further questions, feel free to ask! 😊",
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