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ChatGPT Moderation API: Enter/Output Management | by Andrea Valenzuela | Jul, 2023

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Utilizing the OpenAI’s Moderation Endpoint for Accountable AI

Towards Data Science
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Massive Language Fashions (LLMs) have undoubtedly reworked the way in which we work together with expertise. ChatGPT, among the many outstanding LLMs, has confirmed to be a useful device, serving customers with an unlimited array of knowledge and useful responses. Nevertheless, like all expertise, ChatGPT isn’t with out its limitations.

Latest discussions have dropped at gentle an essential concern — the potential for ChatGPT to generate inappropriate or biased responses. This problem stems from its coaching knowledge, which includes the collective writings of people throughout numerous backgrounds and eras. Whereas this range enriches the mannequin’s understanding, it additionally brings with it the biases and prejudices prevalent in the actual world.

Consequently, some responses generated by ChatGPT might mirror these biases. However let’s be honest, inappropriate responses will be triggered by inappropriate person queries.

On this article, we are going to discover the significance of actively moderating each the mannequin’s inputs and outputs when constructing LLM-powered functions. To take action, we are going to use the so-called OpenAI Moderation API that helps determine inappropriate content material and take motion accordingly.

As at all times, we are going to implement these moderation checks in Python!

It’s essential to acknowledge the importance of controlling and moderating person enter and mannequin output when constructing functions that use LLMs beneath.

📥 Person enter management refers back to the implementation of mechanisms and methods to watch, filter, and handle the content material supplied by customers when partaking with powered LLM functions. This management empowers builders to mitigate dangers and uphold the integrity, security, and moral requirements of their functions.

📤 Output mannequin management refers back to the implementation of measures and methodologies that allow monitoring and filtering of the responses generated by the mannequin in its interactions with customers. By exercising management over the mannequin’s outputs, builders can handle potential points reminiscent of biased or inappropriate responses.



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