You’re likely already aware of AI hallucinations. Perhaps you’ve seen a funny chatbot response blasted on Twitter. While they may be amusing, they do create real risks to AI integrity. Imagine asking an AI for a recipe and it suggests chlorine gas or any poison (yes this actually happened). Not ideal, right?

In this article, we’re going to cover everything you need to know about AI hallucinations, from causes and types to mitigation techniques.  

What are AI hallucinations?

An AI hallucination is when AI systems, such as chatbots, generate responses that are inaccurate or completely fabricated. This happens because AI tools like ChatGPT learn to guess the words that fit best with what you’re asking. But they don’t really know how to think logically or critically. This often leads to confusion and misinformation which is also called “AI hallucinations”. 

What are the main causes of AI hallucinations?

Hallucinations are an inherent risk in large language models (LLMs), stemming from the foundational models developed by OpenAI, Google, Meta, and others. This area is beyond user control, and comes with the GenAI field, so we’ll avoid pointing out the obvious.

Here we’ll focus on looking at the LLM use case that most companies are bringing to market.

RAG (retrieval-augmented generation) LLMs

RAGs are a favorite due to their compatibility as a chatbot engine. While many claim that using RAG can reduce the hallucination problem, it’s not that simple. RAG doesn’t solve hallucinations. Along with the inherent This section outlines the causes for hallucinations specifically associated with the use of RAG:

• Inaccurate context retrieval: When the retrieval mechanism fetches irrelevant or low-quality information, it directly impacts the quality of the generated output, leading to hallucinations or misleading responses.
• Ineffective queries: Poorly formulated prompts from the user can mislead the retrieval process. Additionally, if the GenAI app’s prompt is ineffective, then responses may be based on incorrect or inappropriate context.
• Complex language challenges: Challenges in understanding idioms, slang, or accurately processing non-English languages can cause the system to generate incorrect or nonsensical responses. This is compounded when the retrieval component struggles to find or interpret contextually relevant information in these languages.

AI Hallucinations vs AI Biases: What’s the Difference?

It’s important to separate between AI hallucinations and biases. Biases in AI result from training that leads to consistent error patterns. For example, if an AI frequently misidentifies wildlife photos because it was mostly trained on city images. Hallucinations, on the other hand, are when AI makes up information out of thin air. Both are issues that need addressing, but they stem from different root causes.

5 Types of AI Hallucinations

1. Fabricated content: AI creates entirely false data, like making up a news story or a historical fact.
2. Inaccurate facts: AI gets the facts wrong, such as misquoting a law.
3. Weird and off-topic outputs: AI gives answers that are unrelated to the question. This leads to bizarre or confusing responses.
4. Harmful misinformation: Without prompting it, AI might produce offensive or harmful content.
5. Invalid LLM-generated code: When tasked with generating code, AI might produce flawed or completely wrong code.

Why AI Hallucinations are a big problem

When AI gets things wrong, it’s not just a small mistake—it can lead to ethical problems. This is a big issue because it makes us question our trust in AI. It’s especially tricky in key industries like healthcare or finance, where wrong info can cause real harm. 

Here’s why AI hallucinations matter a lot:

• Misinformation spread: This can mislead users and perpetuate discrimination and fake news.
• Trust erosion: Frequent hallucinations erode trust in AI. This leads to skepticism about its reliability.
• Reliability concerns: This raises doubts about the AI’s capability to consistently provide accurate and reliable outputs.
• Ethical implications: They may amplify biases or lead to questionable ethical outcomes.

In the commercial context, AI hallucinations present additional threats to defend:

• Brand reputation: AI hallucinations can harm a company’s reputation, reducing customer trust and loyalty.
• Product liability: Inaccuracies in critical industries could lead to serious legal issues.
• User experience degradation: Unreliable AI outputs frustrate users, affecting engagement and adoption.
• Competitive disadvantage: Companies with more reliable AI solutions have a market advantage over those with hallucination-prone products.
• Increased costs: Addressing AI hallucinations involves additional expenses, from technical fixes to customer service.

How to mitigate AI Hallucinations?

Reducing the occurrence of AI hallucinations involves several strategies:

1. Implement AI Guardrails: Proactive measures that filter and correct AI outputs in real-time to mitigate hallucinations and prevent malicious attacks. Guardrails ensure in real time the reliability of interactions, safeguarding brand reputation and user trust.

2. Enhance AI knowledge base: Broadening the AI’s training data to include a wider variety of sources can reduce inaccuracies.

3. Robust Testing: Regularly testing AI against new and diverse scenarios ensures it remains accurate and up-to-date.

4. Encourage proof: Users should be encouraged to verify AI-generated information, fostering a healthy skepticism towards AI responses.

Real examples of hallucinations

Google’s Bard hallucination

Google’s Bard AI made a factual hallucination about the James Webb Space Telescope, causing a significant drop in Alphabet Inc.’s market value.

Microsoft Bing’s misinformation

Microsoft’s Bing chatbot provided incorrect information about election-related questions.

ChatGPT’s creative fiction

Instances where ChatGPT generated entirely fake bibliographies or legal citations.

Final thoughts

AI hallucinations present a significant challenge, not just for casual users but for technology leaders striving to make generative AI reliable and trustworthy. Solutions like Aporia Guardrails are key in ensuring AI applications remain accurate, enhancing both user trust and the overall AI experience. By understanding and addressing the causes of AI hallucinations, we can pave the way for more dependable and ethical AI applications.

Written by: Noa Azaria @Aporia

The post “What are AI Hallucinations and how to prevent them?” first appeared on Aporia