LLM Hallucinations – Definition, Examples and Potential Remedies

Reliance on large language model (LLM) technology is becoming more common across many industries, but it often comes with many different examples of hallucination associated with it. What are LLM hallucinations, what causes them and how can their effects be mitigated in LLMs leveraged by a variety of industries?

With the rise in popularity of ChatGPT, Bing, Bard and other AI-based tools in the last two years is it really that surprising that many industries have seen a marked uptick in generative AI development?

Chief among these is the introduction of LLMs, which makes it easier for many organizations to quickly reap the benefits of AI thanks to technologies like LangChain or approaches like soft prompting – just look at some of these AI in healthcare examples.

However, before they get ahead of themselves, organizations need to remember a very apt saying for the times they find themselves in – “everyone makes mistakes.”

AI is a powerful tool after all, but it is not perfect. Therefore, organizations need to take this into account before they begin any kind of LLM Implementation, whether it be for design creation, content generation or some other task. These mistakes are known as LLM hallucinations, but what does this mean exactly, and what are some possible examples of, and fixes for, this problem?

LLM hallucination – definition

An LLM hallucination refers to anything that a program like ChatGPT, Bing, Bard or others generate that is otherwise nonsensical or detached from reality. These hallucinations occur due to faulty data being present in the LLM’s training material or in the library it is drawing from.

The following section will dive more into the causes of these hallucinations, however, the problem here for organizations should be self-evident. Any examples of LLM hallucinations could lead to a bias within the AI system itself, or false information being presented as factual.

Worse, these hallucinations can even cause LLMs to produce discriminatory content from mundane prompts. The question is then – what exactly causes LLM hallucinations?

Causes of LLM hallucinations

Of course, it is important to note that the technology behind ChatGPT, Bing and Bard continues to evolve and, as such, more causes for hallucinations may be found as time goes on, but for now this article highlights five common examples of LLM hallucination below:

• Source-reference divergence – appears when an AI system is allowed to gather data for itself with minimal human intervention, enabling it to draw the wrong conclusion from the data it collects,
• Exploitation through jailbreak prompts – occurs when individuals manipulate flaws in AI systems’ reasoning methodologies to produce outputs that are either unexpected or go way beyond the program’s intended use,
• Reliance on incomplete or contradictory datasets – happens due to the initial dataset the system is trained on containing incomplete, contradictory or outright false information, which leads to confusing or inaccurate outputs being produced by the LLM,
• Overfitting and lack of novelty – appears when the system produces outputs almost identical to the information it was trained on, while being unable to produce any answers outside what it already knows through its own reasoning,
• Guesswork from vague prompt inputs – occurs due to insufficient information being provided by the user, causing the system to fill in the gaps by itself, leading to outputs that are inaccurate or nonsensical.

Now that their origins have been discussed, take a look at some common examples of LLM hallucinations.

Types of LLM hallucination

As stated before, LLMs will continue to get smarter over the years, leading to more complex hallucinations down the line. But, for now, this article will draw attention to the five most popular examples of LLM hallucinations that are slowing down implementation efforts. These hallucination types are:

• Sentence contradiction – occurs when the dataset used to train a system is faulty, leading to an organization’s content arguing against its own stance on a given topic, issue or trend in places, making it nonsensical and harder for readers to understand,
• Prompt contradiction – materializes when outputs do not match the inputs given by users, which in turn highlights the lack of complexity in LLM models overall, resulting in a lack of trust by all users if any contradictions are found,
• Factual contradiction – occurs when datasets are incomplete or inaccurate and results in, potentially, the worst outcomes for any organization, especially as these false outputs often present themselves as factual. This might lead to organizations losing trust with their customers if these hallucinations are not caught in time,
• Nonsensical output – occurs when inputs are too vague or there are gaps in the dataset which in turn highlights the lack of complexity in LLM models overall, resulting in a lack of trust by all users – especially if they are being used in practical, day-to-day operations,
• Irrelevant or random LLM hallucinations – materializes when inputs are vague or datasets are disorganized, resulting in outputs that do not relate to either the user’s input prompts or the output they desire. This leads to a lack of LLM confidence across the board due to its perceived inaccuracy and unreliability.

How to prevent LLM hallucinations?

So far, this article has outlined the causes and examples of various types of LLM hallucinations, but how can organizations prevent them from occurring in the first place or, failing that, mitigate their presence in any LLM system?

Firstly, limiting response lengths and having more control around inputs by implementing suggestions on the home page, rather than leveraging a free-from input box, can help here. Doing this will limit the chances of nonsensical or irrelevant responses from any LLM and provides better instruction to the system, leading to better overall answers.

Adjusting model parameters and leveraging a modulation layer within an LLM is also an option, which ensures a balance between creativity and accuracy while filtering out any inappropriate responses quickly and easily. Establishing feedback from users is also critical in order to fix the areas that need the most improvement at speed. In addition, augmenting both its training and overall dataset with industry-specific information also helps reduce the chances of users encountering any more examples of LLM hallucination while using the system.

Finally, incorporating an LLM with an external database or leveraging contextual prompt engineering is also a viable solution for minimizing LLM hallucinations. However, both options come with their own sets of risks, so developers are advised to do their own due diligence on the quality of information that will drive both options forward before committing to them. Otherwise, they could easily end up with even more examples of LLM hallucinations than they ever had before.

LLM hallucinations – final thoughts

In conclusion, LLMs are here to stay, but the hallucinations that so often come with them do not have to be, so long as organizations take the steps needed to mitigate their impact on their systems as quickly as possible.

Here at Software Mind, we know dealing with the various examples of LLM hallucinations outlined above can be challenging. But do not worry, we understand the benefits of LLMs, what they can do for you and how to implement them in your company at speed. Our proven generative AI services team is happy to talk about what you can do with your data wherever you are – just get in touch with us via this form.