AI hallucination definition: Why it happens & how to prevent it

In Artificial Intelligence (AI), hallucination refers to the generation of false or misleading information by a large language model (LLM). When you interact with AI, sometimes it might produce responses that seem plausible but aren't based on reality or accurate data.

AI hallucinations occur due to insufficient training data or the model's attempt to handle ambiguous queries.

AI hallucinations happen due to the way AI systems learn and make predictions. These models, such as chatbots and image recognition tools, rely on large datasets to identify patterns. If the data used for training is flawed, incomplete or biased, the AI can develop incorrect associations.

This can cause the AI to produce hallucinated responses based on these faulty patterns. Such hallucinations are quite common and may go unnoticed because ‌they appear plausible.

Some of the reasons why AI hallucinations happen are:

• Training data limitations: if the data on which the AI model is trained is incomplete, biased or contains errors, it may generate hallucinated content

• Overfitting: when a model is too closely fitted to its training data, it may fail to generalise well to new data, leading to hallucinations

• Complexity: when faced with complex queries, the model might "fill in the gaps" with invented information, trying to produce a coherent answer

• Inference techniques: some generative AI models that use probabilistic methods to generate responses might create plausible-sounding but incorrect information

AI hallucinations can range from little contradictions to completely made-up information.

1 - Sentence contradiction

Sentence contradiction occurs when an AI generates statements within a single response that contradict each other. For example:

Prompt: "Tell me about the Eiffel Tower."

Response: "The Eiffel Tower is located in Paris, France. The Eiffel Tower is in Rome, Italy."

2 - Prompt contradiction

Prompt contradiction happens when the AI's response contradicts the input prompt. For example:

Prompt: "What is the tallest mountain in Africa?"

Response: "Mount Everest is the tallest mountain in Africa."

3 - Factual contradiction

Factual contradiction hallucination occurs when the AI provides information that's factually incorrect or fabricated. For example:

Prompt: "When was the Great Wall of China built?"

Response: "The Great Wall of China was built in the 20th century."

4 - Irrelevant or random hallucinations

Random AI hallucinations occur when the AI generates information that's irrelevant to the context or is completely random. For example:

Prompt: "What's the weather like today?"

Response: "The capital of France is Paris and it is known for its art and culture."

AI hallucinations are a problem because they can erode user trust, spread misinformation and can impact critical applications. 👇

1 - Erosion of user trust

AI hallucinations disrupt the trust users place in these systems. As users increasingly perceive AI as reliable sources of information, encountering hallucinations can lead to a sense of scepticism.

This erosion of trust can have long-term consequences for user adoption and acceptance of AI technology.

2 - Spread misinformation and security threats

Hallucinations have the potential to spread misinformation, fabricate citations and manipulate user perceptions. This can lead to users becoming susceptible to believing false information generated by AI.

Furthermore, hallucinations could be exploited in cyberattacks, posing security risks to individuals and organisations.

3 - Impact on critical applications

Today, AI is used in critical fields like healthcare, finance and the legal profession. AI hallucinations are a problem because it can lead to erroneous diagnoses, financial losses or legal misjudgments.

Such inaccuracies jeopardise safety and integrity, underscoring the urgent need for mitigation strategies, which we’ll talk about now.

There are several ways to minimise the occurrence of AI hallucinations and make AI systems more reliable 👇

1 - Good-quality data

Use high-quality, unbiased training data to ensure AI models learn accurate patterns and associations.

Quality data minimises the risk of hallucinations caused by flawed or incomplete training datasets.

2 - Clear and specific prompts

Providing clear and well-defined prompts help AI systems understand user intent accurately. These guide AI to generate responses effectively and reduce the likelihood of generating hallucinated outputs due to misinterpretation.

3 - Regular testing and refinement

Conduct frequent testing and refinement of AI models to identify and address potential hallucinations.

Regular evaluation ensures that AI systems remain accurate and reliable over time, with adjustments made to mitigate the emergence of hallucinated responses.

4 - Keep humans in loop

Incorporate human oversight into AI processes to validate outputs and intervene when necessary.

Human judgement adds a critical layer of scrutiny to detect and correct hallucinated responses, ensuring the trustworthiness and integrity of AI-generated information.