When German journalist Martin Bernklautyped his name and location into Microsoft’s Copilot to see how his articles would be picked up by the chatbot, the answers horrified him. Copilot’s results asserted that Bernklau was an escapee from a psychiatric institution, a convicted child abuser, and a conman preying on widowers. For years, Bernklau had served as a courts reporter and the AI chatbot had falsely blamed him for the crimes whose trials he had covered.
The accusations against Bernklau weren’t true, of course, and are examples of generative AI’s “hallucinations.” These are inaccurate or nonsensical responses to a prompt provided by the user, and they’re alarmingly common. Anyone attempting to use AI should always proceed with great caution, because information from such systems needs validation and verification by humans before it can be trusted.
But why did Copilot hallucinate these terrible and false accusations?
They’re bugs. Major ones. Fundamental flaws in the program. People with a vested interest in “AI” rebranded them as hallucinations in order to downplay yhe fact that they have a major bug in their software and they have no fucking clue how to fix it.
It’s an inherent negative property of the way they work. It’s a problem, but not a bug any more than the result of a car hitting a tree at high speed is a bug.
Calling it a bug indicates that it’s something unexpected that can be fixed, and as far as we know it can’t be fixed, and is expected behavior. Same as the car analogy.
The only thing we can do is raise awareness and mitigate.
It actually can be fixed. There is an accuracy to answers. Like how confident the statistical model is on the answer. That’s why some questions get consistent answers while others don’t.
The fix is not that hard, it’s a matter of reputation on having the chatbot answer “I don’t know” when the confidence on an answer isn’t high enough. It’s pretty similar on what the chatbot does when you ask them to make you a bomb, just highjacks the answer calculated by the model and says a predefined answer instead.
But it makes the AI look bad. So most public available models just answer anything even if they are not confident about it. Also your reaction to the incorrect answer is used to train the model better so it’s not even efficient for they to stop the hallucinations on their product. But it can be done.
Models used by companies usually have a higher confidence threshold and answer “I don’t know” if they don’t have enough statistical proof on a particular answer.
This has been tried, it’s helping but it’s not enough by itself. It’s one of the mitigation steps I was thinking of. And companies do work very hard to reduce hallucinations, just look at Microsoft’s newest thing.
From that article:
The Hidrogen from water thing is simply wrong. If that is supposed to mean that hallucinations are just part of a generative LLM technology that cannot be solved.
They are not inherent of the technology. They are a product of lack of control over the stadistical output. Prioritizing any answer before no answer.
As with any statistics you have a confidence on how true something is based on your data. It’s just a matter of putting the threshold higher or lower.
If you ask an easy question “What is the capital of France?” You wont ever get an hallucination. Because all models will have that answer provided with very high confidence. You just have to make so if that level of confidence is not reached it just default to a “I don’t know answer”. But, once again, this will make the chatbots seem very dumb as they will answer with lots of “I don’t know”.
The problem here is the amount of data and the efficiency of the model. In order to get an usable general purpose model with a confidence threshold high enough to not hallucinate, by todays efficiency with the models it would need to be an humongous model, too big and with too much training data even for big tech. So we can go that big, we can try to improve efficiency (which is being proven very hard for general models) or we do both. Time will tell, but I’m quite confident that we will reach a general use model without hallucinations sooner or later.
It’s not a bug. Just a negative side effect of the algorithm. This what happens when the LLM doesn’t have enough data points to answer the prompt correctly.
It can’t be programmed out like a bug, but rather a human needs to intervene and flag the answer as false or the LLM needs more data to train. Those dozens of articles this guy wrote aren’t enough for the LLM to get that he’s just a reporter. The LLM needs data that explicitly says that this guy is a reporter that reported on those trials. And since no reporter starts their articles with ”Hi I’m John Smith the reporter and today I’m reporting on…” that data is missing. LLMs can’t make conclusions from the context.