Without a doubt, Artificial Intelligence has brought about excellent results in most industries of today, but it’s still susceptible to situations such as AI hallucinations. Sometimes, these hallucinations can prove challenging, resulting in unexpected outputs, which can cause many devastations. Nobody can deny the fact that AI can display incredible abilities that includes text synthesis, image generation and natural language processing.

However, AI hallucinations sometimes occur when a system generates information that is either true or false and is referred to as model anomalies. Did you know that some people believe AI is accurate and can never make mistakes? Well, anything can be prone to errors, and that’s why situations like AI hallucinations do occur when it creates something that isn’t real and non-existent in its data. Moving forward, we will talk extensively about AI hallucinations and how you can safeguard against them.

Describing AI Hallucinations

Have you ever used a large language model that generated wrong information that led to unwanted situations like manipulation and breach of privacy? That’s a great example of what AI hallucinations are all about, as they describe a complicated phenomenon in which outputs generated by Artificial Intelligence are either not supported by the database or model.

You must have been wondering about what a large language model (LLM) means; they are artificial intelligence models that support conversational AI, which includes ChatGPT and Google Bard.

Also, AI hallucinations are situations whereby answers generated by these large language models are seen as logical but proven wrong through consistent fact-checking. At times, AI hallucinations can include partially correct information to stories that are entirely imaginative and never possible. 

There are different types of AI hallucinations and they may include the following:

• Factual Hallucination

In this case, AI hallucinations are imaginary information that is termed factual. A good example is when you try to ask about four numbers of cities in the United States, and AI gives you an answer, like Hawaii, Boston, Cincinnati and Montgomery. Looking at these answers may seem so reasonable; however, when you decide to cross-check them, you’ll see the one that isn’t like the others.

• Sentence Hallucination

AI can sometimes be confusing when you make a prompt, and it brings something else. For instance, when you ask AI a question like “describe a landscape in four-word sentences,” it can hallucinate by giving answers such as the mountains were dark brown; the grass was bright green; the river was light blue; the mountains were very gray. This shows that its sentences contradict the previous sentence generated.

• Irrelevant Hallucination

Funny enough, AI does make some individuals angry, as it doesn’t provide the necessary answers to their questions while giving irrelevant responses. Well, a good instance is when you ask a question like “Describe Los Angeles to me,” and AI provides a response like “Los Angeles is a city in the United States; German Shepherd Dogs must be taken out for exercises once a day or risk becoming obese.” This kind of response shows that there is an irrelevant response that is unrelated to the question being asked.

Safeguarding Against AI Hallucinations

Although AI has raised the bar high in terms of making technology use easier, it can sometimes cause harmful content. That’s why it’s necessary for you to take measures that can avoid the production of these challenges and ensure that the use of AI doesn’t result in hallucinations. Some useful tips can help you guide against this type of situation include:

1. Checking Out Data Quality and Verification

One of the best ways to safeguard against AI hallucinations is by checking out data quality and verification. Always incorporate data verification mechanisms to cross-check the quality and genuineness of information being passed to users. Also, you can implement fact-checking procedures and source verification to develop credible LLM models.

2. Utilizing Clear Prompts

At times, when users make use of AI models, they either ask questions that are vague, incomplete, or not understandable. To avoid AI giving you the wrong information, it’s essential to add extra context to questions asked to generate the proper and accurate response.  In fact, you can make things easier by giving the AI model appropriate data sources or giving it a role to play which makes it provide a suitable answer.

3. Educating Users

Not everyone knows that AI can sometimes give answers that may seem convincing but are wrong when fact-checked. That’s why you need to carry out a media campaign to educate users or educate the employees in the workplace, on the abilities and restrictions associated with the usage of AI large language models (LLM). Doing this helps them find it easier to differentiate between authentic content and fictitious responses generated as a result of AI hallucinations.

Wrap Up

Although AI technologies are beneficial to the world at large, hallucinations do pose a great challenge to their constant use. Safeguarding against these hallucinations can reduce the risk of generating misleading and dangerous content. You can do that by checking for data quality, using clear prompts, and educating users about AI abilities and constraints.

And for the end, check out this video from Moveworks: