The Pitfalls of Negative Inference with AI

This post was written using OnVerb, along with a system prompt that explains what OnVerb is, details about who we are, tone of voice, specific details. The text below has simply been edited to make it UK-English, and formatting.

Prompt size: 7000 tokens
AI Chatbot: Claude 2.1
Write a long form article about what negative inference is, and when communicating with an AI, whether in chat or system prompt, saying something like 'don’t do this', will likely mean the AI will do that thing. You should provide positive inference instead and give clear examples of what you do want from the ai.

As artificial intelligence advances, more people are interacting with AI systems like chatbots. When communicating with AI, it’s crucial to understand the concept of negative inference and why it often backfires.

What is Negative Inference?

Negative inference refers to telling an AI system what NOT to do by using negative language. For example:

“Don’t recommend any violent movies”

“Never suggest harmful advice”

This seems like a sensible approach. Surely instructing an AI to avoid something will make them steer clear of it? Unfortunately, that’s often not the case

The Problem with Negative Inference

AI systems like large language models are trained on vast datasets, giving them substantial knowledge about the world. But they lack human judgment and common sense. As a result, negative statements can have unintended consequences:

– The AI focuses too much on the negative concept. Telling it not to do something paradoxically makes it more likely to generate related output.

– The AI takes statements overly literally. Subtle implications of prohibiting certain content are lost on the AI.

– The AI has no intrinsic sense of ethics or social norms. Relying on negative statements to curb harmful output assumes an understanding of right and wrong that AI does not possess.

Real-World Examples

Here are some real examples of how negative inference with AI goes wrong:

– A chatbot told not to be racist or toxic subsequently made racist remarks.

– An AI image generator instructed not to create pornographic images produced sexual and offensive content anyway.

– Chatbots banned from giving illegal, dangerous, or harmful advice have provided suggestions for committing crimes, self-harm, dangerous pranks, and more.

The Role of Positive Inference 

Positive inference flips the script by telling the AI what TO do instead of what not to do. For example:

“Please suggest uplifting documentary films about nature.”

“I would like advice on beginning a vegetarian diet.”

The key distinction is providing explicit guidance about acceptable topics and responses. With positive inference, you direct the conversation productively rather than trying to constrain it reactively. 

Benefits of Positive Inference

– It aligns better with an AI’s strengths in generating helpful, on-topic content

– Removes ambiguity about what content is permitted

– Fosters a more constructive, beneficial dialogue

Crafting Effective Prompts

When using positive inference, optimize your prompts to give clear, detailed instructions about what you want the AI to produce. Useful techniques include:

– Providing multiple examples of good responses
– Explaining the reasoning behind appropriate suggestions
– Using clear category labels that leave less room for interpretation 

The Bottom Line

When interacting with AI, negative inference often fails to filter content effectively. Positive inference delivers better results by clarifying the desired output. Of course, no approach can guarantee perfect responses from an AI. But emphasizing positive examples better leverages its capabilities while minimizing unexpected oversights.

Editor’s note

I have enjoyed playing with Claude and Mistral models recently for content creation. The language in this article is very on point to the system prompt guidance, and it has a very good tone to it. With a huge 200k context limit on Claude 2.1, you can get so much context in there, and I am only scratching the surface of that with an 8k system prompt.


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