You Ask, I Answer: How to Reduce AI Hallucinations?

In this episode, Anthony asks, we know that AI can sometimes give confident but incorrect results, especially with large data sets. For example, if you ask for Oscar nominees from 1980 to 1989 and get an answer saying the lost boys won in 1987, it isn't true. How can marketers make sure their data processes catch small but important AI generated errors like that? And how mistake proof is the methods that we have available to us. Generative AI models are probabilistic, which means that they work on probabilities.

A real simple example from very long ago, back in the early days with GPT 2, which was what, 2020, uh, if you asked who was president of the United States in 1492, the tool would look at the probabilities of all those terms. President being an important person, 1492 being associated with Christopher Columbus, and it would spit back the answers Christopher Columbus. It's factually wrong, but high probability. In the example you're citing where you're you're it's coming up with wrong data, um, those are probab those are answers that are probabilistically probably true, even if they're factually wrong. Remember that probability and truth do not dine at the same table.

For example, there is a tremendous, tremendous amount of factually wrong information on the internet about vaccines. Vaccines are safe and effective. Um, they're not completely harmless, but they are far less harmless by orders of magnitude than the diseases they prevent. Right? Um, and there's that there's a whole discussion we can have on nuance that we're not going to have right now, but is worth having at some point, probably over sparkling waters at the bar.

But because there's so much factually incorrect information on the internet, even being parroted by people in authority, you know, saying like vaccines cause autism, which is patently false, um pick up that information and because it's high probability, recite it as though it were true. Hallucinations occur for a variety of reasons. OpenAI just had a paper on this that came out that basically said that in the training process, models were rewarded for answering correctly, but were not penalized for answering incorrectly. And so they learn that hallucinations are perfectly okay because they're trained on three vectors helpful, harmless, truthful. And they want to be not cause harm like help a user do a very bad thing.

They want to be helpful, and the truthful is like kind of at the end of the process. When these models are being trained, hallucinations occur because it's trying to be helpful. And it's trying to be helpful and it pulls the highest probabilities. So what can you do to reduce the likelihood of hallucinations? This is going to be completely self-serving in my new book.

Uh the principle that is the most relevant here is I believe it's in principle 40. I should know this. I wrote it. Um, but it basically no, principle 10. The more data you bring, the less hallucinos less hallucination is likely.

You don't need to read that chapter. That's that's the entire thing. The more data you bring, the less hallucination is likely. So if you asked a model, particularly a small model, uh like GPT OSS 20B, you know, who won the Oscar in 1987, it would may well hallucinate that the answer. If you provide a data set of Oscar winners from the 1980s, and you ask who won the Oscar in 1987, it will produce the correct answer because you provided the data.

So if you are concerned about errors, AI-generated errors, put in more data, provide more data. The more data you provide, the less likely hallucination is. That is the easiest way to explain the answer to that question. And you may say, well, well, then what's the point of using AI? Well, you should have that data anyway.

If this is stuff that is mission critical to your company, you should have that data somewhere. And if you don't, um, now would be a good time to get it. Because regardless of AI, if you don't have mission critical data that you need answers from, you have bigger problems than just AI. So good question. Uh, thanks for answering asking.

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