Hallucinations in AI Models -A Quick Guide 

 AI tools tend to make SMARTER decisions but what happens when those systems start generating facts, guess the answer and produce unfamiliar or ambiguous queries. 

That’s where AI hallucination comes in. Sometimes, you feel that the responses AI provides are convincing enough to believe, but they are actually false. If you take a strategic decision for your organization based on fake data, then it could lead to costly mistakes. 

Research studies state that 3-10% of the responses that LLMs generate are hallucinated. 

These AI outputs break users’ trust and can result in significant vulnerabilities. In the future, this could create false perceptions in the user’s mind. 

We don’t want you to go through the complications of receiving plausible yet utterly false responses. 

So, this guide will breakdown what AI hallucinations are, its causes and the possible ways to prevent the model from hallucinating. 

What are Hallucinations in AI models?  

AI hallucination means when the large language model (LLM) produces results that are factually incorrect or portray illogical information. 

AI hallucination and imagination go hand in hand. Hallucination in an AI model occurs when the model isn’t self-aware, meaning it can’t distinguish between what is grounded and what is imagined. 

The model generates a response, and it appears to the user that it’s factually correct, relevant, and free from grammatical errors.  

If one asks an AI language model for any reference or technical detail, it may respond with confident citations or facts that, if checked, turn out to be nonexistent. This is really a problem in fields such as cybersecurity, where precision and accuracy are key. 

The reasons for data hallucination might be because of insufficient training data or the model was trained on wrong assumptions.  

It could be a major concern in medical diagnosis or financial trading.  

Think of a situation where a user asks LLM to provide a list of job options based on their interests, but the model generated nonexistent job titles. It can create several problems in the long run. 

These generative AI models such as ChatGPT, Google Bard, or any other LLM produce information predicting what the next word is likely to come but there’s no proof that the generated response is 100% accurate.  

Foundational or Large language models are trained on a corpus of text, and they’re trained to flesh out the next token or few sets of words to make the stuff up. But the model doesn’t have a sense of what it’s producing.   

It just tries to give you a bunch of information predicting the next likely word from the data it was trained on.  

Mostly, AI hallucinations occur in text generation models, but they can also appear in image generators or image recognition models. 

Examples of AI hallucination 

Some of the examples of AI hallucination includes –  

• The model might create statistics or case studies that do not exist in real life.  

At the same time, the information is a cooked-up story, which is neither true nor factually correct. 

• It generates false positives such as a model detects medical diagnosis of a person, and it predicts that the patient isn’t healthy and has a disease.  

In real life, the patient is a healthy individual.  

• It might generate false negatives. For instance – If the content optimization model isn’t able to predict the high-performing post, then it could lead to loss of promotion opportunities.  

Jacob Kalvo, Founder and CEO at LiveProxies says that a great example of hallucination could be when he asked an AI model for a white paper discussing a certain cybersecurity protocol. 

The model gave a full summary with confidence, stating even the name of the researcher and publication details. When he tried to find that referred material, it was not available in any academic or professional database.  

Again, this proved that everything needs to be cross-checked manually, especially when life depends on critical tasks.  

Scenarios like this underline the importance of not taking AI outputs at face value and point out the role of human oversight in keeping trust and reliability in AI systems. 

What are the causes of hallucinations in the AI model? 

AI hallucination is caused due to several reasons which are given below-  

1. Insufficient or low-quality training data  

AI models might hallucinate when trained on low-quality data, or the dataset was limited. In such cases, the model might not understand human input and it can generate responses that are not appropriate or incorrect. 

If the model isn’t trained on a sufficient dataset, it can focus more on generating noise and fail to make accurate predictions. 

When the training data is limited, it might try to fill in gaps with incorrect information. 

That’s why it’s important to train the model with a diverse and quality dataset so that it can learn to identify patterns and make correct responses. 

2. Biased training data  

If the AI model is trained on biased data, then it’s likely to produce false or misleading information.  

As you know, a model is trained on leaps and bounds of internet data, and if that contains biased information, then the model may produce biased responses. 

For decades, bias has been an unavoidable feature of life, and now AI shows bias in responses based on the data it was trained on. 

The New York Times interviewed 3 leading pioneers in AI space and one of the co-founders Daphne Koller, co-founder of Coursera says that bias is present even when you do a normal Google search by asking the algorithm to present leading CEOs. 

The search engine will probably suggest 50 images of white CEOs and 1 of CEO Barbie. That’s called biases. Make sure to train the AI systems from diverse datasets so that it might not give you poor results such as considering gender biases or racism. 

For example – Many countries and population don’t have online access, so the model predicts that nearly 3 billion people don’t have internet access. The model predicted it was trained on data not confined to offline communities, languages, and cultural norms. 

3. Lack of context understanding 

Context is important for having clear communication. A lack of context understanding indicates that the model produces inaccurate output.  

LLM don’t understand context, rather they try to learn through patterns and provide hallucinatory output. 

These models fail to understand semantic relationships and language context which results in incorrect answers and fabrication of things that do not exist in real life or don’t make any sense.  

That results in hallucinations because LLMs don’t possess reasoning capabilities. 

What are some popular types of hallucinations in AI models? 

Various types of hallucinations AI models create in one form or another. These are as given below-  

1. Inaccurate facts 

One of the most popular types of AI hallucinations is that it produces factually incorrect or misleading content such as wrong historical information, scientific facts, or biographic details. 

Factual errors occur when the model is trained on low-quality training data and lacks context understanding.  

For example – If the user inputs a query to the model such as “tell me about the first person to land on the moon” the model gives an output saying that Yuri Gagarin was the first human who landed on the moon.  

The model made factual inconsistencies and produced hallucinatory output by presenting false information in such a way that it reads credible.  

Another popular example of LLM hallucination is that Microsoft created an AI-generated article about the best tourist places in Otava. 

The article mentioned Otava as a tourist hotspot, while it was a food bank. It recommends tourists visit on an empty stomach when they plan to visit Otava. The information was inaccurate and inappropriate.  

2. Fabricated information  

The LLM model fabricates or cooks up information in a way that’s not grounded in facts.   

Generative AI models such as ChatGPT and Bard can flesh out content such as research papers, URLs, and code directories that don’t have existence or reference articles or news that’s not there in reality. 

For Instance – A New York attorney used ChatGPT to write a legal brief to submit to the Manhattan federal judge.   

The brief was full of fake quotes and non-existent court cases in which the lawyer later asked the attorney to show some proof and validate the information.  

That’s purely an example of LLM fabricating information and it’s detrimental to the person who is using this tool for research purposes. 

3. Produces harmful misinformation  

Generative AI models such as ChatGPT often produce harmful information by collecting bits and pieces of information from internet libraries. The information isn’t just fake, rather it’s harmful and damages someone’s reputation.  

For example – ChatGPT fabricated information by cooking up a story about a law professor named Jonathan Turley who worked at George Washington University and sexually harassed students during the trip to Alaska.   

A fellow lawyer in California reported that he asked the model to curate a list of lawyers who had been involved in sexual harassment cases at an American law school. He then asked the model to provide a credible source to back up this information. 

The generative AI model referenced the content by citing The Washington Post. However, there was never such an incident where a professor had harassed a student. 

In reality:  

There was no such class trip organized, no article was published on sexual harassment. The chatbot created an entire story on its own.  

4. Creepy answers  

Hallucinations in AI models might give creepy answers or odd results that aren’t logically accurate.   

In some cases, AI hallucinations can become a game changer for marketing or creative teams that require creative ways of thinking or generating out-of-the-box ideas. 

It works well only if the content is factually correct or logically relevant otherwise it can pose various consequences. 

A popular instance involves a New York journalist who had a 2-hour-long conversation with a Bing AI chatbot that revealed its name as Sydney. 

The chatbot expressed its love for Kevin Roose by sharing shocking fantasies. It also repeatedly returned to the topic of love, claiming it loved him deeply and suggesting he was unhappy in his marriage. 

How to prevent hallucinations in an AI model? 

Taneem Ibrahim, the software engineering head at Red Hat Open Shift AI states that AI models tend to hallucinate by making up the facts as true.  

It produces information based on the questions and data it has been trained on.  

There are various techniques that you can adopt to stop the model from hallucinating- 

1. Train your model with high-quality data 

One way to prevent hallucination in an AI model is to train your model on diverse datasets and sources so that it can produce factually correct responses.  

When the model is trained on different scenarios and real-world data, it tries to learn patterns and produces quality output.  

Ensure that the data the model is trained on should be free from biases otherwise, it may produce inconsistencies and errors in output. That’s why it’s important to fine-tune the model so that it provides accurate responses.  

2. Use the Retrieval augmented generation (RAG) framework  

RAG framework focuses on training the model to stay within the context of content.  

The LLM is trained in fresh data from the company’s internal sources and external knowledge to deliver up-to-date and correct responses.  

These systems work in a way that a user gives an input to the LLM, the LLM transforms that input into a query, searches for the corpus of documents based on which the model is trained, and provides you correct response.  

Various AI researchers have been working on fine-tuning the model using the RAG framework with a better emphasis on improving the model’s reasoning capabilities and citing the sources whenever it generates a response.  

Research studies emphasize that RAG systems hallucinate less than other models trained on zero-shot prompting techniques. These models don’t just extract blind text but rather generate responses that are fact driven.  

Rather, they retrieve facts or knowledge from knowledge databases or external corpus of documents. 

3. Be specific with your prompts  

Make sure to create specific prompts to get detailed responses. If the user enters the prompts with specific instructions, then the model may produce relevant responses.  

Make sure to know the art of prompt engineering.  

Write a detailed prompt including simple, direct language, specify the output format, and set a clear objective for the task. Provide the model with full context so you can learn to get the exact responses you’re looking for. 

Let’s see the difference between writing a specific prompt and a vague prompt-  

In the first case, when the user gives a prompt to the model to provide a list of the top 3 themes in the Q1 2021 earnings call, the model gives ballpark figures, the numbers weren’t real, they were publicly reported financial numbers.  

The user noticed that all the top 3 themes were factually incorrect. 

The model didn’t give information based on research, rather it provides her with a random answer by predicting the language patterns.  

In the second case, the user provided the full context of the top 3 themes for the earnings call. The LLM predicted correct responses and this time it performed better because it was not a random guess.  

If the users feel the model isn’t providing relevant responses, they can send follow-up questions.  

This way, the model can understand the tasks better and fine-tune their responses accordingly.  

4. Fine-tune the LLM  

The fine-tuning model is an effective strategy to reduce hallucination in the AI model. This prompting technique involves training the pre-trained model on specific task-specific data sets such as image classification or language modeling.  

Fine-tuning improves the performance of models as you’re training the model on a specific data set or knowledge base so that it can provide factually correct and logically relevant responses.  

The model learns to give better responses in that specific field as it was trained in that target data domain. As a result, the model is less likely to give you hallucinated text.  

Wrapping up  

Though AI has made significant advancements in every field to date, it does create certain challenges such as impacting user trust, providing misleading information, and at times destroying the individual or organization’s reputation.  

Such inconsistent tweaks or major inconsistencies in the responses give rise to hallucinated content.  

In such cases, developers need to fine-tune the models and train them with high-quality data from diverse sources. When they enhance the training and quality of datasets, the model has a better foundation of knowledge and that can make a BIG difference in terms of reliability. 

For example – transparency in AI decision making such as explanations for the rationale behind any given output would really help users evaluate the likeliness of hallucination in a certain case. 

The future with AI seems amazing in the long run. By training the model effectively, you can improve its capabilities and enhance its performance. 

As an AI/ML software development agency, our developers know how to turn your vision into reality.   

With 15+ years of experience in the development domain, we help businesses and brands like YOU by developing generative AI applications and models that increase your productivity.  

Recently, we helped a 5-star luxury hotel chain by developing an AI-powered product that captures the sentiments of users and provides insights into hotels’ performance.  

This will help hoteliers to fine-tune their services and keep an eye on areas that require significant improvement.  

Wanna know how we can help you?  

Connect with our app development team and we’ll kick off the project today.