What Causes AI Hallucination and How to Avoid It?

Generative AI systems, such as chatbots and text generators, have rapidly advanced in capability, yet they are not without their flaws. These AI tools can often produce unexpected results, particularly for users who are still figuring out the most effective ways to prompt them.

Giving AI models too much leeway can lead to the generation of bad and incorrect data. In this article guide we will investigate the reasons behind phenomena such as AI hallucination and offer strategies to mitigate these issues, aiming for the production of reliable content.

So, what is AI hallucination?

AI hallucination refers to the tendency of Large Language Models (LLMs) to produce responses that are not accurate. These inaccuracies can vary from slight factual errors to entirely fabricated information.

This problem is widespread, to the extent that prominent generative AI systems like ChatGPT include disclaimers about the potential for "inaccurate information regarding individuals, locations, or facts."

AI systems such as ChatGPT base their responses on predicting the most logical subsequent word in a reply, according to the user's input (or prompt). Given these models do not possess the ability to reason independently, their predictions can sometimes be off the mark. Consequently, the final output may deviate significantly from factual accuracy.

These deviations can be hard to spot, largely because language models are adept at generating text that is fluent and coherent, leading users to mistakenly trust the accuracy of the response. Hence, verifying the accuracy of the information provided by an AI model is essential to ensure your content remains free of falsehoods.

Causes of AI hallucination

The phenomenon of AI hallucination arises from the use of substandard or inadequate training data. Essentially, the performance and output of a generative AI system mirror the quality of the data it has been trained with. Therefore, if the training data contains gaps or lacks coverage for certain scenarios, the AI may produce incorrect outputs.

A notable issue leading to AI hallucination is overfitting. This occurs when an AI system becomes overly familiar with the training data to the point where it struggles to adapt to new, unseen data. Consequently, when the AI is tasked with generating outputs based on new information, it may generate incorrect or fabricated information.

To put it in simpler terms, consider an AI asked to write a product description for a mobile phone but has only been trained on data about fruits. Due to its overfitting to fruit data, it might lack the necessary knowledge to accurately draft a product description, possibly omitting critical elements or inventing others.

Additionally, language poses its own set of challenges. AI systems need to keep up with the evolving nature of language, including new words, slang, and idioms, to avoid misinterpretation.

For optimal results with AI, using straightforward and simple language in prompts is advisable.

Problems associated with AI hallucination

The issue of AI hallucination extends beyond a mere programming error; it has tangible consequences that can affect a brand's reputation negatively. The spread of false information can erode consumer trust, potentially requiring extensive efforts to rebuild. Furthermore, reliance on an AI that frequently produces inaccurate outputs necessitates extensive fact-checking, which could be more time-consuming than conducting original research.

The risks associated with AI-generated content misleading readers are especially pronounced in topics related to finance and personal well-being, known as YMYL (Your Money, Your Life) subjects. Google prioritizes content that demonstrates a high level of Experience, Expertise, Authoritativeness, and Trust (E-E-A-T) for ranking purposes. Therefore, any misinformation can negatively affect your search engine optimization (SEO) performance. Furthermore, inaccuracies in content can potentially harm the reader's health or financial status. However, this doesn't imply that you should avoid using AI in content creation. The key is to minimize the occurrence of so-called AI hallucinations to ensure that the content it generates is both accurate and reliable.

Here are some strategies to curb the likelihood of AI generating misleading information:

1. Supply detailed context

AI systems need clear context to produce precise outcomes. Ambiguous instructions can lead to unpredictable and often irrelevant content. It's essential to clearly articulate what you want the AI to do, providing a comprehensive overview of the topic at hand. Including specific details and sources in your instructions can guide the AI in sourcing its information accurately, thereby diminishing the chance of producing erroneous content.

For instance, rather than a general prompt like, "Write an introduction to an article about the computer electronics industry," a more detailed instruction would be, "Craft a 150–200-word introduction for an article discussing the current trends in the computer electronics industry, intended for publication on an SEO-focused blog. Emphasize advancements in AI and GPU hardware. The tone should be both engaging and authoritative, incorporating statistics from credible tech sources regarding industry trends."

2. Integrate data sources

To prevent your AI model from deviating from factual information, direct it to specific, credible sources for data retrieval. This practice not only ensures the reliability of the information but also saves time you might otherwise spend on manual research. Emphasize the use of authoritative sources to minimize the risk of misinformation. Suggesting specific websites for the AI to research, can further refine the accuracy of the generated content.

3. Minimize mistakes

In addition to providing AI with explicit instructions, it's essential to establish boundaries for the responses you expect. Vague queries can lead to misunderstandings and potentially incorrect information, known as hallucinations. To mitigate this, consider framing your questions with specific choices rather than leaving them open-ended.

This approach narrows down the AI's focus to specific data, reducing the likelihood of errors. For instance, instead of asking broadly about changes in unemployment, specify the years you're interested in, such as comparing unemployment rates between 2019 and 2020 based on government statistics. Similarly, instead of inquiring about the ideal amount of content for your website, ask about the average number of blog posts published by businesses monthly.

When questioning the effectiveness of testimonials on sales, prompt the AI to compare the trustworthiness of testimonials versus advertisements, backed by recent studies. This strategy helps ensure the AI searches for precise information rather than generating responses based on general knowledge.

4. Define roles

Using role play when designing prompts can significantly enhance the output of AI by providing it with additional context that influences both the style and substance of its responses. This technique can lead to more accurate information as it encourages the AI to adopt the perspective of an expert in the field.

For instance, asking the AI to assume the identity of a seasoned digital marketing specialist with a focus on local SEO can produce more tailored and insightful advice for a small business lacking an online presence, especially when budget constraints are considered.

This approach often results in more nuanced and applicable advice compared to more broadly formulated requests for local SEO guidance. Encouraging the AI to embody expertise and supplying detailed context can enhance the precision of its responses.

5. Use negative prompting

Limiting AI-generated "hallucinations," which arise from overly creative interpretations or flawed training data, can be effectively managed through what's known as "negative prompting." Although more common in image generation, this strategy is equally valuable in text generation.

By clearly outlining not only what you expect but also what you wish to exclude from the AI's responses, you can more finely tune the output. Instructions might include avoiding outdated information, steering clear of financial or health advice, or ignoring specific unreliable sources.

Introducing such constraints helps refine the AI's output to better meet your requirements while addressing potential inaccuracies. This proactive approach necessitates anticipation of possible misdirections by the AI, a skill that improves with practice and experience in communicating with the technology.

6. Modify AI response creativity through temperature control

Many users are unaware of the temperature control feature in AI tools, such as Yazo AI, which is pivotal for managing how creative or straightforward the AI's responses are. By adjusting the temperature, you can directly influence the level of randomness in the responses, which is beneficial for reducing errors or "hallucinations" that might occur. The temperature scale goes from 0.1 to 1.0, where a higher value equates to increased creativity in responses.

For content that strikes a balance between being factual and creative, setting the temperature between 0.4 and 0.7 is advisable. Lower settings will lead to more predictable and accurate content. Although this concept might sound complex, adjusting the temperature is straightforward—simply specify your desired setting to your AI tool. Tools like Yazo AI offer adjustable AI parameters, which is why they excel in producing high-quality AI-generated content.

7. Verify generated content

It's essential not to blindly trust and replicate the content generated by AI tools. Always check the accuracy of the information before making it public to prevent spreading incorrect facts due to AI-generated errors.

Current efforts are underway to address these inaccuracies, but it remains uncertain when substantial advancements will be achieved. The possibility of completely solving the issue of AI making errors, or "hallucinations," is a topic of debate among specialists.

The phenomenon of AI hallucination can be particularly problematic when dealing with Your Money or Your Life (YMYL) topics, which include crucial financial and medical advice. The potential for harm is high. Given the serious implications of spreading misinformation in these areas, not to mention the potential negative impact on SEO rankings due to Google's stringent policies on YMYL content accuracy, it's vital to exercise caution. While AI can still serve as a useful tool for generating initial drafts on these subjects, it's crucial to meticulously verify any factual claims made by the AI to ensure reliability and accuracy.

Bill Gates has expressed a hopeful view in a July blog post discussing the potential dangers AI poses to society, while Emili Bender from the Computational Linguistics Laboratory at the University of Washington has pointed out that such errors may stem from a fundamental mismatch between the technology's capabilities and its intended applications.

Bad impact of AI hallucination on society

The inaccuracies generated by AI can range from amusing to seriously misleading. There have been several instances where AI chatbots disseminated incorrect information about historical events, public figures, and established facts.

One incident in April 2023 involved ChatGPT falsely stating that Brian Hood, an Australian mayor, had been imprisoned for bribery charges. Although Hood was associated with the bribery case mentioned, he was involved as a whistleblower, not as a convict.

Google Bard also made a notable error by inaccurately stating that the James Webb Space Telescope captured the first image of an exoplanet, a claim that was corrected by NASA shortly after its public demonstration. 

In 2016, Microsoft's Twitter AI bot Tay began to produce racist tweets within a day of its release after picking up language from interactions with users, leading to its shutdown by Microsoft.

These incidents underscore the ongoing challenges in achieving full reliability in AI technology, highlighting the importance of scrutinizing AI-generated content.

Conclusion

Artificial intelligence has seen significant advancements recently, yet it’s clear we’re still navigating through its developmental phase. Owing to this, employing AI with oversight from humans is advisable. Utilize it to streamline the content creation workflow and enhance productivity, but always validate the final output to guarantee the integrity and trustworthiness of your content.