Hallucinations in AI – with ChatGPT Examples

4.9/5 - (7 votes)

Hallucinations in the AI context refer to AI-generated experiences, like text or images, that do not correspond to real-world input, leading to potentially false perceptions and misleading results for users.

The term was coined in a 2018 ICLR paper written by Google’s AI Research group:

πŸ’‘ Hallucinations in Neural Machine Translation  - Agarwal et al. (ICLR 2018)

Neural machine translation (NMT) systems have reached state of the art performance in translating text and are in wide deployment. Yet little is understood about how these systems function or how they break. Here we show that NMT systems are susceptible to producing highly pathological translations that are completely untethered from the source material, which we term hallucinations. Such pathological translations are problematic because they are are deeply disturbing of user trust and are easy to find with a simple search. We describe a method to generate hallucinations and show that many common variations of the NMT architecture are susceptible to them. We study a variety of approaches to reduce the frequency of hallucinations, including data augmentation, dynamical systems and regularization techniques, showing that a data augmentation technique significantly reduces hallucination frequency. Finally, we analyze networks that produce hallucinations and show that there are signatures in the attention matrix as well as in the stability measures of the decoder.

Here’s a quick example of a hallucination interacting with ChatGPT (GPT-4):

Example of a Hallucination (founders two and three) from framing the question in a way that makes ChatGPT confidently answer the question in a wrong way.

Artificial intelligence (AI) systems like ChatGPT have transformed the way people interact with technology. These advanced AI models, however, can sometimes experience a phenomenon known as artificial hallucinations.

πŸ’‘ A critical aspect to consider when using AI-based services, artificial hallucinations can potentially deceive users with seemingly genuine, yet false perceptions generated by the AI. In the context of AI, such as chatbots, the term hallucination refers to the AI generating sensory experiences that do not correspond to real-world input.

Introduced in November 2022, ChatGPT has significantly impacted various industries, including healthcare, medical education, biomedical research, and scientific writing. As the technology continues to evolve, understanding the implications of artificial hallucinations becomes increasingly important.

Identifying potential risks and limitations can help ensure that AI systems like ChatGPT continue to improve and are used to their fullest potential in a responsible manner.

πŸ§‘β€πŸ’» Recommended: ChatGPT – 7 Prompt Engineering Tricks

Hallucination in Artificial Intelligence

Definition and Concept

Hallucination in artificial intelligence, particularly in natural language processing, refers to generating content that appears plausible but is either factually incorrect or unrelated to the provided context (source).

This phenomenon can occur due to errors in encoding and decoding between text representations, inherent biases, and limitations in the AI model’s training data (source).

Large language models (LLMs) like ChatGPT can display impressive knowledge depth and fluency, but hallucination problems can hinder their overall usefulness (source). These issues often arise from the AI system’s lack of real-world understanding and the model’s tendency to generate diverse responses that may deviate from the true context.

AI hallucinations can have implications in various industries, including healthcare, medical education, and scientific writing, where conveying accurate information is critical (source). Steps like refining model training and employing verification techniques can be taken to minimize hallucinations.

However, it remains a challenge for researchers to strike a balance between maximizing the model’s potential and preventing hallucination issues.

ChatGPT as an Example

ChatGPT, a widely known AI language model, exhibits signs of hallucination in certain circumstances. These hallucinations can mislead users and raise concerns about the effectiveness and reliability of AI models in general.

In this section, we’ll explore specific examples of hallucinations along with the development and background of ChatGPT.

Examples of Hallucinations

GPT-3.5 solves this riddle wrongly assuming it is a trick question. However, ChatGPT with GPT-4 solves it correctly.

Hallucinations in ChatGPT can range from minor inaccuracies to completely erroneous responses.

For example, ChatGPT might generate a plausible-sounding answer to a factual question that is completely incorrect, such as an erroneous date for the creation of the Mona Lisa (source).

These hallucinations can potentially deceive users into accepting false information as truth, which is particularly risky when dealing with sensitive or high-stakes topics.

Counter Examples

Even though there are many sources referring to hallucinations in the newest ChatGPT model, I couldn’t reproduce many of them. Here are some trick questions where hallucinations didn’t occur in my tests:

Development and Background

ChatGPT is built upon the foundation of large language models (LLMs) that have been proven to hallucinate in some instances (source). The concept of hallucination in AI was popularized by Google AI researchers in 2018, and it has since remained a significant challenge in the development of these models.

As AI technology advances and the potential use cases for language models like ChatGPT expand, it is crucial to be aware of the risks and limitations associated with hallucinations in AI systems.

Mitigating Hallucination in AI Systems

Techniques and Approaches

There are several techniques and approaches to mitigate hallucination in AI systems, such as ChatGPT. One common method is combining multiple approaches to reduce output errors in AI’s reasoning. AI researchers work on refining models and incorporating additional data to minimize inaccuracies resulting from hallucination issues.

Another approach is using an ensemble of various AI models. By leveraging the strengths of individual models, the overall system’s reliability and output quality can be improved. This reduces the likelihood of AI hallucinations

Examples of Success

As AI developers continue to work on minimizing and eliminating the chances of AI hallucinations, some advancements have already been made.

For instance, OpenAI’s second-generation language model, GPT-3, is an example of how continuous improvements in AI development can lead to more accurate and less hallucinatory AI models. GPT-3’s performance has surpassed its predecessor, GPT-2, offering better text-generation capabilities and fewer occurrences of artificial hallucination.

GPT-4 is even better in regards to hallucinations and I bet the following versions that may be already out when you are reading this have reduced the probability of hallucinations even more.

Furthermore, ongoing research in the field seeks to develop novel techniques and methodologies to address hallucination. Collaborative efforts between researchers, developers, and the AI community help in creating improved versions of AI systems that are better at handling hallucinations and delivering accurate results.

Ethical Considerations

When discussing artificial hallucinations in AI systems like ChatGPT, it’s crucial to consider the ethical implications of their use. As AI-powered chatbots continue to advance, concerns regarding the potential for misinformation or harmful content increase.

One key ethical consideration is the risk of AI-generated content spreading false information or creating misleading perceptions. Researchers and developers must work to minimize this risk by implementing appropriate safeguards in AI systems like ChatGPT.

Another related concern is the potential for AI-generated content to undermine trust in genuine information sources. As AI becomes more sophisticated, distinguishing between AI-generated and human-generated content becomes increasingly challenging. This could lead to skepticism and uncertainty when evaluating the veracity of online information.

Moreover, the ethical use of AI-generated content in sensitive domains, such as healthcare or education, should be carefully considered. In these fields, misinformation or misinterpretations can have significant consequences for individuals’ well-being or professional reputations. Developers should prioritize incorporating Ethical AI principles in creating and deploying AI applications.

Considering the potential risks associated with AI-generated content, it is essential to ensure that regulatory frameworks and accountability mechanisms are in place. These measures can help protect users from unintended consequences while supporting innovation in AI technologies.

Addressing these ethical considerations requires collaboration between researchers, developers, policymakers, and industry stakeholders. By working together, it is possible to create AI systems that can generate content responsibly while respecting ethical standards and social norms.

Future Directions

As artificial intelligence continues to develop, there are numerous potential avenues for the improvement and application of ChatGPT-like models. In scientific writing, AI-based language models can greatly contribute to generating insightful and well-articulated content by automating certain writing tasks, thus allowing researchers more time to focus on their work (source).

In order to mitigate the risk of digital hallucinations, AI developers should strive to enhance the robustness and context awareness of these chatbots. Addressing the limitations of current AI models will involve advancements in natural language understanding, context awareness, and decision-making abilities (source).

Moreover, it is essential to prioritize ethical considerations in developing AI-powered chatbots. Some key issues include ensuring transparency, ensuring respect for privacy and security, addressing the potential of AI to produce misinformation, and minimizing biases in training data (source).

There are several possible future applications of AI chatbots in different industries:

  • Healthcare: AI could provide medical advice or suggest initial diagnoses, freeing up healthcare professionals’ time for more complex cases.
  • Education: Students could receive personalized learning experiences by engaging with AI chatbots as tutors or study aides.
  • Customer service: AI could handle many customer inquiries, reducing the need for large customer service teams.

Ultimately, the ongoing development of artificial intelligence, including chatbots like ChatGPT, presents a wide range of exciting opportunities as well as significant challenges that must be carefully considered (source).


In summary, artificial hallucinations in AI systems like ChatGPT can arise due to the vast amounts of data the models have been trained on, which may include inaccurate or unusual content (source). Such models may provide responses that appear delusional or disconnected from reality, posing challenges and risks in AI ethics and applications.

Despite the issues associated with hallucinations in AI, advancements in AI technology like GPT-4 continue to be impressive and impactful (source). As developers and researchers work on mitigating these hallucination-related issues, users are encouraged to remain cautious and vigilant when engaging with such AI systems.

The AI community must collaborate on refining these models to reduce unintended consequences, such as artificial hallucinations (source). Efforts should also be made to educate users on the potential risks and limitations of artificial intelligence language models. Continued research and responsible development will ultimately pave the way for more reliable and trustworthy AI applications.

πŸ€– Recommended: Artificial General Intelligence — A Helpful Guide