When AI lies: the hallucination problem explained

Artificial intelligence (AI) has made remarkable strides in recent years, demonstrating impressive capabilities in natural language processing, image generation, and decision-making. Yet, beneath the shimmering surface of progress lies a stubborn and increasingly worrying flaw: AI hallucinations.

In AI, a “hallucination” refers to a chatbot or language model generating false, misleading, or entirely fabricated content while presenting it as fact. The term has become a lightning rod in the AI world – both technically and philosophically – as hallucinations continue to plague even the most advanced systems from companies like OpenAI, Google, and DeepSeek.

As OpenAI’s reasoning models o3 and o4-mini show, even the latest and most powerful AI tools are not immune. In fact, they may be worse. Read more about the recent surge of AI hallucinations in the article “AI’s hallucination problem is getting worse”.

What Are AI Hallucinations?

At its core, an AI hallucination is a disconnect between the AI's output and reality. They occur when a model generates outputs that are factually incorrect, logically inconsistent, or entirely fabricated. These aren't just minor errors or slight misinterpretations; they are confident assertions of falsehoods. These hallucinations can be subtle – such as minor inaccuracies in a response – or extreme, where the AI invents events, people, or scientific findings that do not exist.

Hallucinations are particularly common in large language models (LLMs) like OpenAI’s GPT series, Google’s Gemini, and Anthropic's Claude models. These models generate text based on probabilities learned from vast datasets, but they do not inherently “understand” truth or reality. Instead, they predict the most statistically likely next word or phrase, which can sometimes lead to falsehoods.

The key characteristic of a hallucination is the AI's certainty. It presents the fabricated information as if it were a well-established fact, often without any hedging or indication of uncertainty. This can make these hallucinations particularly misleading, as users might be inclined to trust the AI's seemingly authoritative tone.

Examples of AI Hallucinations

An AI might invent historical events or scientific facts, misinterpret idiomatic meaning and provide false translations, or even generate plausible-sounding but entirely fictional product descriptions. Here are some examples of AI hallucinations that often occur:

False Citations in Research

AI models sometimes generate fake references – citing academic papers that do not exist or misattributing findings to the wrong authors. This can be problematic for researchers relying on AI for literature reviews.

Incorrect Math or Logic

Despite improvements, some models still hallucinate during basic arithmetic or multi-step logic problems. This isn't just a matter of a slight miscalculation; it can involve fundamental errors in arithmetic, algebraic manipulation, statistical interpretation, or logical deduction.

Fabricated News Stories

AI-generated news articles may contain invented events, people, or statistics, leading to misinformation. This is particularly concerning in journalism and political discourse.

Incorrect Medical Advice

AI models trained on medical data may provide hallucinated diagnosis or treatment recommendations, which can be dangerous if users rely on them without consulting professionals.

Unrealistic AI-Generated Images

Generative AI models like DALL·E or Midjourney sometimes create distorted or impossible images, such as people with extra limbs or text that is unreadable.

Causes of AI Hallucinations

The reasons behind AI hallucinations are complex and are still an active area of research. However, we can point to several key contributing factors:

1. Training Data Limitations

AI models are trained on large datasets, but these datasets may contain errors, biases, or incomplete information. If an AI encounters gaps in its knowledge, it may attempt to fill them with plausible but incorrect information. This can lead to overgeneralization or overfitting.

1.1. Overgeneralization

LLMs work by predicting the next word in a sequence. They are trained to find patterns and relationships in the data and generate text that flows logically. Sometimes, in the absence of concrete information, the model might “fill in the blanks” in a way that sounds coherent but is factually wrong. It's like a storyteller embellishing a tale to make it more engaging, even if it deviates from the truth.

1.2. Overfitting

This occurs when a model becomes too specialized in the training data and performs poorly on new, unseen data. In the context of hallucinations, an overfitted model might latch onto specific patterns in the training data, even if those patterns don't hold true in general.

2. Lack of Up-to-Date Information

Most AI models operate based on a fixed training dataset and lack real-time access to new information. As a result, they may miss recent developments or rely on outdated sources. This can lead to hallucinations, especially when the model attempts to answer questions about events or knowledge that emerged after its training cut-off. In trying to provide a complete response, the model may also supplement its answers with information that is no longer accurate or relevant.

3. Contextual Misunderstanding

While AI models are getting better at understanding context, they can still misinterpret nuances in a user's query. This can lead to the model generating an answer that is relevant to a slightly different question, drawing incorrect inferences, or lacking factual basis.

4. Reinforcement from User Interaction

If users do not challenge incorrect responses, AI models may continue generating similar hallucinations. Some models also learn from interactions, reinforcing incorrect patterns over time.

Implications of AI Hallucinations

The consequences of AI hallucinations can range from minor inconveniences to serious risks:

• Misinformation: Fabricated information can spread rapidly, leading to misunderstandings and potentially harmful decisions, especially in areas like health or finance.
• Breach of trust: If users repeatedly encounter inaccurate information from AI systems, their trust in the technology will inevitably erode.
• Bias amplification: If the training data contains biases, hallucinations can manifest as biased or discriminatory outputs, further reinforcing harmful stereotypes.
• Creative misdirection: While sometimes hallucinations can lead to unexpected and creative (albeit incorrect) outputs, in many professional contexts, accuracy is paramount.
• Legal and regulatory challenges: Governments and organizations are increasingly scrutinizing AI-generated content. Hallucinations may lead to legal liabilities, especially if AI systems provide false information that harms individuals or businesses.

Mitigating AI Hallucinations

Overcoming AI hallucinations is a significant challenge, but researchers are actively working on various strategies. It requires effort from multiple stakeholders, including developers, professional users, and everyday users. Each group can contribute in different ways.

Developers and Researchers

Developers are at the forefront of building more reliable AI systems and are employing several key strategies to mitigate hallucinations:

Improved Training Data

Curating higher-quality, more comprehensive, and less biased datasets is essential. This includes rigorous data cleaning, fact-checking, and incorporating a wide range of trustworthy sources to reduce knowledge gaps and minimize misleading content.

Enhanced Model Architectures

Developing more sophisticated model architectures that have a better understanding of context and are less prone to generating nonsensical information is an ongoing area of research. One promising approach is retrieval-augmented generation (RAG), which equips AI models with access to external databases, documents, or real-time information.

Built-In Fact-Checking Systems

Embedding real-time verification mechanisms into AI systems allows models to cross-reference output with trusted sources before presenting it to users. This is particularly important for applications where factual accuracy is critical.

Model Refinement and Feedback Loops

Incorporating user feedback – such as flagging hallucinated responses – helps retrain and fine-tune models, improving reliability over time.

Professional Users

Professional users, who often rely on AI for specific tasks and have a deeper understanding of its capabilities and limitations, can employ advanced techniques:

Retrieval-Augmented Generation (RAG)

While developers build RAG-enhanced systems, professionals can apply the tools that combine large language models with external databases or real-time search systems. RAG allows models to “look up” facts instead of relying solely on memorized data, improving factual accuracy in technical, legal, medical, or scientific contexts.

Adjusting the “Temperature” Setting

The “temperature” setting in AI models is a crucial hyperparameter that controls the randomness and creativity of the model's output. Lowering the temperature makes the model more predictable and less likely to generate improbable or nonsensical content, consequently reducing the occurrence of hallucinations. However, the ideal temperature setting depends on the specific task and the desired balance between accuracy and creativity.

Cross-Validation with Trusted Sources

For high-stakes tasks, it's crucial to verify AI-generated content against trusted data, industry guidelines, or authoritative publications before using or sharing the information.

Everyday Users

Common users, while not having direct control over model development, can adopt several practices to mitigate the impact of AI hallucinations:

Prompt Engineering Techniques:

• Explicitly ask for factual information and verification: Phrase prompts to encourage the AI to provide factual answers and, if possible, to indicate its level of certainty or the sources of its information.
• Use negative constraints: By telling the AI what not to include or what kind of answer the user doesn’t require can sometimes reduce the likelihood of the AI wandering into speculative or fabricated details.
• Ask the AI to justify its reasoning: Prompting the AI to explain the steps it took to arrive at an answer can help identify potential logical flaws or unsupported assumptions.

User Awareness and Critical Evaluation

Recognizing that AI can hallucinate and critically evaluate the information provided, cross-referencing with reliable sources when accuracy is important.

Providing Feedback

Utilizing any feedback mechanisms provided by AI platforms to report inaccuracies or potential hallucinations, contributing to the overall improvement of the models.

Embracing the Unexpected

AI hallucinations are a fascinating yet challenging aspect of artificial intelligence. They highlight the fact that while AI systems can be incredibly powerful and helpful, they are not infallible sources of truth. AI models are sophisticated tools that learn from data and generate responses based on patterns, but they don't possess genuine understanding or common sense. This inherent limitation often leads to the generation of fabricated realities, which, in most contexts, are undesirable.

However, beyond the pitfalls, researchers and creative professionals are beginning to explore the intriguing potential of AI hallucinations. Scientists have found that, in specific domains, the ability of AI to generate novel, albeit factually incorrect, connections can serve as a valuable tool for scientific discovery, particularly in fields requiring innovative and unconventional approaches to complex problems. The unexpected leaps and associations made by “hallucinating” AI might spark new hypotheses or perspectives that human researchers might not readily consider.

Furthermore, while unwanted in factual applications, the creative potential inherent in AI hallucinations presents a range of intriguing use cases that can help organizations leverage its imaginative capabilities in positive ways. Generating imaginative and surreal imagery, revealing unexpected patterns and offering fresh perspectives on complex data, creating novel, unpredictable, and immersive virtual environments – all that and more underscores the dual nature of AI hallucinations.

As we continue to refine AI models to minimize inaccuracies, it's equally important to understand and potentially harness the unique, albeit sometimes erroneous, outputs they can produce. The future of AI will likely involve navigating this delicate balance: striving for truth while embracing the unexpected sparks of innovation that even its “lies” might ignite.

Iryna Tkachenko, marketing manager