What is AI illusion and why does it affect the accuracy of AI-generated content? Find out the causes, risks and effective ways to prevent them.
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Home page » Articles » AI hallucination: Causes and ways to prevent Hallucination phenomenon
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Although large language models (LLMs) bring many important advances to artificial intelligence, certain limitations still exist. In some cases, the model can generate false or unfounded information but still present it with a high level of confidence, a phenomenon known as AI Hallucination. Within the scope of this article, FPT.AI will explain the concept in detail, analyze the root cause and propose solutions to minimize the problem of AI illusion.
AI Hallucination or Hallucination is a phenomenon in which artificial intelligence models, especially large language models (LLM), create content that is misleading, irrelevant, or completely fabricated but still presented in a fluent and convincing manner. The model itself is not capable of recognizing the truth or falsehood of information, only predicting and reproducing learned data patterns. Therefore, sometimes results will be produced that far exceed reality.
Unlike traditional programming errors that can be directly identified and fixed, AI illusions come from the operating mechanism of the model. This is not a single technical error, but a natural consequence of the system's learning and content generation process.
>>> Learn more: AI Ethics – Building a responsible AI future
Below are some outstanding situations that show how AI illusions can appear in a variety of contexts. These examples illustrate how large language models generate information that sounds reasonable, but is in fact misleading or non-existent.
Answering knowledge questions: When answering questions about people or events, the model may generate inaccurate biographical information. For example, AI can confirm that an actor has won an Oscar even though that person has never actually been nominated. This bias often occurs when the model infers based on similar data samples rather than on proven information.
Generate text content: During the process of writing articles on historical or academic topics, AI may add bogus details. For example, when writing about World War II, the model may depict a battle that never happened or give misleading casualty figures. These details make the content look complete and convincing, but they reduce the credibility of the information.
Programming support: When interpreting libraries or generating source code, the model sometimes creates functions or methods that do not exist. For example, an AI may describe a method in a popular Python library even though that library does not actually provide such functionality. This can cause confusion and slow down software development if users do not double-check the official documentation.
AI illusions are often classified into three common types for ease of analysis and evaluation. However, the boundaries between these forms are not completely separate. In fact, a hallucination can have many different characteristics at the same time. For example, an AI-generated story may contain both false information and fabricated or illogical details.
Factual bias: Occurs when an AI model provides information that is inconsistent with validation data, such as in fields such as history, science, or mathematics. Even advanced models can have difficulty maintaining consistent accuracy.
Fabricated content: Appears when the model does not have enough information to respond but still creates a fictional story to complete the response. This phenomenon often occurs in topics that are less popular or lack reference data. In addition, combining many individual pieces of information into an accurate conclusion is also challenging, causing the model to tend to "fill in the blanks" with bogus details.
Nonsensical answers: In some cases, AI can produce answers that are grammatically correct and visually coherent but lack substantive meaning. This situation often appears when the input question contains contradictions or is unclear. The reason lies in the way language models work: they predict word sequences based on learned data patterns instead of actually understanding the content. So the results may be smooth and convincing, but lack logic or not convey useful information.
AI illusions do not appear randomly but originate from many factors related to training data and the model's operating mechanism. Understanding the root causes will help us appreciate the limits of AI and find ways to mitigate this phenomenon. Below are the main causes of AI illusion.
An important cause of AI illusion is incomplete or biased training data. Large language models depend on data to learn patterns and generate responses. When the data is inaccurate or does not cover a topic, the model tends to make inferences and produce incorrect information.
This problem is often evident in highly specialized fields such as science, where quality and diversity of data is limited. If the training set is poor or the description is vague, the model will easily learn the surface instead of grasping the essence, leading to overfitting and hallucinations.
Besides, bias in the process of collecting or labeling data can distort the model's perception. An imbalanced data set, biased towards a certain perspective, will cause the AI to reproduce those biases in its answers.
Language has a complex structure with many layers of context and nuances of expression, requiring AI models to process beyond surface sentence patterns. When the model architecture lacks depth or representational capacity, it is difficult to fully capture context and prone to generating simplistic or misleading responses.
This limitation makes it easy for the model to misunderstand the meaning of words and phrases, thereby providing inaccurate interpretations. The problem is even more pronounced in highly specialized tasks, where deep reasoning capabilities are required. Therefore, inappropriate model architecture is an important cause of AI illusion.
Overfitting makes it difficult for AI models to adapt to new contexts and easily produces rigid or irrelevant responses. This phenomenon occurs when the model remembers the details of the training data instead of learning to generalize the rule.
Although deep learning can improve accuracy on old data, it is detrimental when the model processes new inputs, especially in flexible command environments with many different expressions. When overfitted, the model easily repeats irrelevant information and still exhibits false confidence. This problem is often evident in specialized domains, where high-quality training data is limited, increasing the risk of AI illusions.
Text generation methods such as beam search and sampling also contribute to the AI illusion. These techniques are designed to maximize fluency and text variety, but often at the expense of accuracy.
Cluster search prioritizes high-probability word sequences, which helps keep sentences coherent but can create content that sounds plausible but untrue, especially in tasks that require high precision. In contrast, the sampling method introduces a random element into the text generation process, increasing creativity but also increasing the risk of meaningless or fabricated content.
Therefore, balancing fluency, creativity, and reliability in generative AI is a big challenge. In sensitive fields such as medicine or legal, it is necessary to incorporate additional information verification mechanisms to minimize the risks caused by AI illusions.
The AI illusion could have significant impacts as generative AI tools are increasingly widely applied in business, academia, and life. In high-risk fields, false or fabricated information from AI can undermine trust, lead to wrong decisions and lead to serious consequences, specifically:
The phenomenon of AI illusion can be limited by a combination of measures, including improving the quality of training data, optimizing and adjusting models, verifying output, enhancing coordination between humans and AI, as well as designing input commands appropriately.
Training data quality plays a key role in minimizing AI illusions. A diverse, representative, and low-bias data set helps the model access a variety of contexts and nuances, resulting in more accurate and reliable responses.
To achieve this, developers need to apply a rigorous data selection process. For example, eliminating unreliable sources, periodically updating data, and adding content that has been vetted by experts. At the same time, techniques such as data augmentation and active learning can help detect information gaps and improve the quality of the training set, while bias control tools help maintain data balance and representativeness.
Fine-tuning and calibrating the model is an important step to reduce AI illusions and improve system reliability. These processes help tailor model behavior to intended use, reducing errors and increasing the relevance of output. In particular, fine-tuning allows general model customization for specific tasks, helping the model operate more stably in each context.
Many methods support this process, notably reinforcement learning from human feedback (RLHF), in which the model is improved based on user evaluation. Besides, parameter adjustment helps control the style and firmness of the response. Techniques such as dropout, regularization and early stopping are also applied to limit overfitting, helping the model generalize better instead of just memorizing the data.
Verifying AI output by comparing it with a trusted source or proven knowledge helps detect and correct errors promptly, thereby limiting potential risks. This approach is especially important in fields that require high precision such as education and research.
Besides, integrating human review into the operating process creates an additional layer of necessary supervision. In high-risk fields like healthcare or legal, this moderation mechanism helps ensure that results from AI are carefully reviewed before being put to use.
From the user's side, the way the command is designed directly affects the degree of appearance of AI illusions. Vague statements often lead to misleading or irrelevant responses, while clear and specific requests help the model produce more accurate and useful results.
To increase output reliability, users should apply appropriate statement design techniques, such as breaking complex tasks into simple, manageable steps. This approach reduces ambiguity and limits the risk of errors occurring during response generation.
Research in the field of AI is focusing on developing more transparent and reliable models to reduce AI illusions. An important approach is explainable AI (XAI), which allows clarifying the basis for forming responses, thereby helping users evaluate the reliability of the results.
In addition, the Retrieval-Augmented Generation (RAG) architecture combines a generative model with external data sources to provide answers based on verified and updated information. This approach reduces the risk of fabrication and improves the accuracy of the output.
AI Hallucination is a reminder that although very powerful, artificial intelligence still has limitations and continues to be perfected. To effectively exploit the potential of AI and ensure sustainable development, we need to promote research, improve technology and build a transparent and responsible AI usage ecosystem. The journey towards truly trustworthy AI systems is still long, so humans need to take an active role in evaluating and controlling the results generated by AI.
References:
Google Cloud. (n.d.). What are AI hallucinations? https://cloud.google.com/discover/what-are-ai-hallucinations?hl=en
IBM. (n.d.). AI hallucinations. https://www.ibm.com/think/topics/ai-hallucinations
The Conversation. (n.d.). What are AI hallucinations? Why AIs sometimes make things up. https://theconversation.com/what-are-ai-hallucinations-why-ais-sometimes-make-things-up-242896
The New York Times. (2025, May 5). AI hallucinations: ChatGPT, Google. https://www.nytimes.com/2025/05/05/technology/ai-hallucinations-chatgpt-google.html
VnExpress. (2024, April 15). Illusion phenomenon leads to AI fabrication. https://vnexpress.net/hien-tuong-ao-giac-dan-den-ai-bia-dat-4864534.html
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