AI startup Mistral has launched a new API for content moderation, which can be tailored to specific applications and safety standards. The API is powered by a fine-tuned model (Ministral 8B) trained to classify text in a range of languages, including English, French, and German.
API Details
The moderation API can be applied to either raw or conversational text. Mistral claims that its API is highly accurate but admits it’s a work in progress. The company didn’t compare its API’s performance to other popular moderation APIs like Jigsaw’s Perspective API and OpenAI’s moderation API.
Key Features
- Customizable: The API can be tailored to specific applications and safety standards.
- Multi-language support: The model is trained to classify text in English, French, German, and potentially other languages.
- High accuracy: Mistral claims that its API is highly accurate but admits it’s a work in progress.
Challenges with AI-powered Moderation
AI-powered moderation systems are useful in theory but also susceptible to the same biases and technical flaws that plague other AI systems. For example, some models trained to detect toxicity see phrases in African American Vernacular English (AAVE) as disproportionately ‘toxic.’ Posts on social media about people with disabilities are often flagged as more negative or toxic by commonly used public sentiment and toxicity detection models.
Mistral’s Approach
Mistral claims that its moderation model is highly accurate but also admits it’s a work in progress. The company didn’t compare its API’s performance to other popular moderation APIs like Jigsaw’s Perspective API and OpenAI’s moderation API. Mistral says it’s working with customers to build and share scalable, lightweight, and customizable moderation tooling.
Batch API
Mistral also announced a batch API today. The company says it can reduce the cost of models served through its API by 25% by processing high-volume requests asynchronously. Anthropic, OpenAI, Google, and others also offer batching options for their AI APIs.
Industry Developments
Over the past few months, there has been growing enthusiasm across the industry and research community for new AI-based moderation systems. These systems can help make moderation more scalable and robust across applications.
Conclusion
Mistral’s launch of its content moderation API is a significant development in the field of AI-powered moderation. While the company claims that its API is highly accurate, it admits that it’s a work in progress. The industry still has much to learn about AI-powered moderation systems and their limitations.
References
Related Topics
- Content Moderation
- Generative AI
- Mistral
- Moderation
Recent Developments in AI-powered Moderation
Over the past few months, there has been growing enthusiasm across the industry and research community for new AI-based moderation systems. These systems can help make moderation more scalable and robust across applications.
Industry Enthusiasm for AI-powered Moderation
The industry’s enthusiasm for AI-powered moderation is driven by several factors. First, AI-based moderation systems have shown promise in detecting hate speech, harassment, and other forms of toxic content. Second, these systems can be trained on a large dataset of labeled examples, making them more accurate over time.
Challenges with AI-powered Moderation
While AI-powered moderation systems have their benefits, they also have several challenges. For example, some models trained to detect toxicity see phrases in African American Vernacular English (AAVE) as disproportionately ‘toxic.’ Posts on social media about people with disabilities are often flagged as more negative or toxic by commonly used public sentiment and toxicity detection models.
Mistral’s Approach
Mistral claims that its moderation model is highly accurate but also admits it’s a work in progress. The company didn’t compare its API’s performance to other popular moderation APIs like Jigsaw’s Perspective API and OpenAI’s moderation API.
Batch API
Mistral also announced a batch API today. The company says it can reduce the cost of models served through its API by 25% by processing high-volume requests asynchronously. Anthropic, OpenAI, Google, and others also offer batching options for their AI APIs.
Conclusion
Mistral’s launch of its content moderation API is a significant development in the field of AI-powered moderation. While the company claims that its API is highly accurate, it admits that it’s a work in progress. The industry still has much to learn about AI-powered moderation systems and their limitations.
References
Related Topics
- Content Moderation
- Generative AI
- Mistral
- Moderation
