• By quade
• 5 July 2024

Scientists Might Have A Solution to AI Hallucinations Affecting Text Prompts

Although Artificial Intelligence capabilities have improved by leaps and bounds, several persistent problems remain that hold it back. Among the biggest issues that experts have noted include its energy usage and reliance on new data. But perhaps the biggest of these that stop mainstream adoption is AI hallucinations.

With a solution to this problem, there is a chance it might increase the reliability of AI systems and attract more businesses to invest and adopt them. However, it will depend on how successful this solution is.

What Are AI Hallucinations?

LLMs, such as ChatGPT are designed as chatbots that produce answers to your questions. However, they are programmed to produce the language, not necessarily facts. This means while it can create text that sounds perfect, it may not be backed by any data or facts. When this happens, it is called AI hallucinations because the system will confidently and concisely claim something completely false.

Fixing the problem is not easy because most LLMs are capable of producing realistic-looking text that it requires constant fact-checking to see whether what it says is true or not. Because of that, it is difficult to determine the success of each solution deployed.

However, this is what is holding back many people from embracing AI. If they cannot even trust what it produces, then it will not be able to automate many tasks because there is no guarantee that what it produces is legitimate.

What Can Be Done About AI Hallucinations?

When LLMs give inaccurate answers, it is most often because they do not understand the question or do not have data to give a knowledgeable answer. To combat, AI hallucinations, scientists have created a solution called “confabulations”.

This works by having a second LLM study the work of the first and fact-checking it, allowing the system to monitor itself without having a human constantly interfering. The second LLM will analyze the meaning of the text and determine its accuracy by having researchers explain what needs to be checked whether that’s what the statement says or if it’s supposed to suggest certain ideas.

The paraphrases could explain the message of the original text and show where the AI hallucination might have happened. Adding a third LLM to evaluate the work can also help as some research suggests this is just as effective as having a human fact-checking all these documents. 

How Can This Help With AI Adoption?

If they can solve AI hallucinations, it can go a long way in boosting AI adoption across different industries as it will bring multiple advantages. The biggest of these is that it can open LLMs to handling more tasks, knowing that they are less likely to solve issues.

More importantly, it can also boost trust in AI systems. One of the biggest advantages of AIs is that they can automate tasks and free up humans to do other things. The problem with AI hallucinations though is that if someone needs to fact-check what the AI says, it defeats automation’s purpose. 

Without worrying about that, it can free up users to handle other tasks and allow companies to put more trust in AI systems.

However, it isn’t all good news as there are still some risks. Some scientists warn that this system might only make AI hallucinations worse if all the LLMs malfunction. This was captured perfectly by a research paper from the University of Melbourne that was published in the Nature Science Magazine.

“Researchers will need to grapple with the issue of whether this approach is truly controlling the output of LLMs, or inadvertently fuelling the fire by layering multiple systems that are prone to hallucinations and unpredictable errors.”

Karin Verspoor, Researcher from the University of Melbourne, in an accompanying article.

How Can Fix AI Hallucinations Benefit BPOs? 

Among the businesses that stand to benefit from fixing AI hallucinations is the IT BPO outsourcing service industry. These companies depend on companies outsourcing their labor to them, which means being able to provide services quickly and efficiently.

Using AI is already a big help in automating services as it allows BPOs to run leaner services without too many employees. Being able to rely on the services more can further improve BPO efficiency. 

Adapting AI can also help global BPO services by leveling the playing field. Even in less developed countries, AI can help cover some of the gaps in knowledge and skill, allowing smaller BPOs to match the services provided by larger ones.