What is an LLM Hallucination?
Why Should We Care?
Learn what LLM hallucinations are, why they occur, and how to prevent them in AI models. Explore solutions to minimize inaccuracies and improve AI reliability.
GPT-4, BERT, and other Large Language Models (LLMs) have dominated the AI field. These models streamline business operations, improve personal productivity, and reshape how we interact with technology. LLMs are a giant leap in AI, solving complex queries or generating human-like text.
However, despite their strength, LLM’s still have some significant downsides. Among those are “hallucinations.” And no, LLM hallucinations have nothing to do with a sense of psychedelic visions. They refer to a situation where a model makes up or presents false data and reports it as true.
Indeed, according to the New York Times, ChatGPT makes things up about 3% of the time. If you have ever asked an AI model a question, received nonsense, or even an entirely fabricated answer, you’ve encountered an LLM hallucination.
But what are these hallucinations, and why should we care about them? This article will delve into the causes of these illusions and their consequences in different fields. We’ll also share how to stop LLMs from putting our trust in AI technology at risk.
Language Model?
I What is LLM Hallucination?
An LLM hallucination occurs when a large language model produces something seemingly true or reasonable that is false, fabricated, or logically flawed. Unlike human hallucinations (sensory perceptions), LLMs simply fail to grasp the reference points, becoming disoriented and presenting text that does not exist as fact.
You could, for instance, query an LLM with a question like: “Who won the 2022 Nobel Prize in Physics?” Rather than saying “I don’t know” or providing an accurate answer, it could quickly and confidently make up a name and the reason for their win. This behavior is especially concerning in industries that require accuracy, like healthcare, law, and finance.
The issue with LLM hallucinations is how realistic the models can make them seem. Widely used language models can predict the next part of a sequence, like which word should come after others, based on probabilities. The more data they are fed, the better they learn to emulate what human speech looks like. However, when given blank text to fill in with the information it doesn’t know, the model generates hallucinations—often without users realizing it.
1. Real-Life Analogy
Think of it like a confident storyteller who doesn’t always check their facts. They might tell you with conviction that a particular historical event took place or that a scientific breakthrough occurred, but unless you fact-check, you won’t know if they’re telling the truth or making things up. LLMs do something similar, generating plausible-sounding information that isn’t always true.
2. Statistics on LLM Hallucinations
Recent studies have shown that hallucinations in LLMs are more common than previously thought. It was found that around 27% of the time, models like GPT-4 contained some form of hallucination, especially when tasked with generating highly specific or niche information.
II Types of LLM Hallucinations
LLM hallucinations can be distinguished based on their etiology and the false information involved. Understanding how these hallucinations work allows us to see why they happen and what might mitigate them. The main types of hallucinations observed in LLMs and their specifics are as follows.
Factual Hallucination
Logical Hallucination
Contextual Hallucination
Predictive Hallucination
Semantic Hallucination
1. Factual Hallucination
During a factual hallucination, an LLM produces data that is not true, or that has been entirely made up. This happens when the model has no external knowledge base or live data, and the responses are generated based on its training data. If questioned on something not inside its training data or where the model does not have enough good data, it would make things up beyond the truth.
Large language models like GPT-4 are trained on huge amounts of text data from many places (books, articles, websites), but they cannot access the internet or external databases at run time. Therefore, for questions beyond its training cutoff or that require an exact factual answer, it may “hallucinate” by producing a sound but false response.
These hallucinations arise largely from interpolation and extrapolation. The model tries to complete missing information by interpolating from learned patterns in the training or extrapolating, predicting relationships it has never seen. This may fail to achieve an accurate answer, particularly with more complicated or niche subjects.
Example
When asked, “Who coined the word “art”?” an LLM might provide fabricated names and facts, especially if the training data only includes information up to 2020.
Mitigation Strategy
One possible solution is integrating models with external knowledge bases or real-time data access, enabling fact-checking against up-to-date information.
2. Logical Hallucination
A logical hallucination is an output where the response is internally inconsistent or illogical despite being grammatically correct. This occurs when the model generates a sequence of words or ideas that don’t logically follow the input prompt or contradict basic rules of logic or reasoning.
LLMs are built on probabilistic models and use pattern matching rather than deep reasoning. While they excel at mimicking human language, they lack true cognitive reasoning or semantic understanding. This limitation leads to logical hallucinations when they cannot generate a response based on a true understanding of the problem.
A simple example is if you have a transformer model like the GPT; it learns probability distribution over a given number of existing words for predicting the next word. The self-attention mechanism helps it weigh the importance of various tokens in input. But it doesn’t understand the logical structure of an argument. In this way, the model can get things right in terms of fluency, yet it fails to provide coherent outputs for something that “makes sense,” especially when reasoning over multiple steps.
Example
When posed the query, “If a car progresses at a velocity of 60 miles for 2 hours, what distance was covered?” a model might respond, “90 miles,” despite the clear mathematical calculation that should yield 120 miles.
Mitigation Strategy
Methods including reinforcement learning augmented by human feedback can help curtail logical leaps by scoring the rational accuracy of model outputs. Additionally, fine-tuning dedicated datasets focused on bolstering consistent logical thinking, such as those involving mathematical, scientific, and reasoned scenarios, can aid in reducing such inconsistencies.
3. Contextual Hallucination
These hallucinations fall under the somewhat-coherent-but-just-not-quite-there category due to an LLM misreading context and returning a related but wholly wrong answer. Hallucinations are also seen in problems with multiple meanings (e.g., engaged and fall) or when the prompt is ambiguous.
Contextual hallucinations happen because of the limitations in context windows in transformer models. The LLM cannot promptly record each token within a fixed-length context and then “see” that token while learning about any subsequent future or conditional tokens from the current prompt. Therefore, the model has a harder time correlating that input to its output response if either an ambiguous term is used or we give it too long of a prompt, which causes it to lose sight of what was even asked.
Semantic drift is another factor. During multiple iterations over the prompt, based on the language tokens that have been processed so far by this model, it might change its understanding of the user’s intention, especially if there are many valid ways to interpret a given input. When a user asks something (with the keyword “Python”), the model could decide whether he means a programming language or a snake, and it will focus on this context.
Example
A prompt like, “Tell me about Apple,” may result in an answer about the fruit when the user intends to learn about the tech company.
Mitigation Strategy
These hallucinations can be mitigated by prompting engineering (where users develop more detailed prompts to steer the model). Disambiguation strategies on ambiguous inputs (either via user feedback or usage history) that help them better interpret ambiguous inputs based on user feedback or previous interactions.
5. Semantic Hallucination
A semantic hallucination happens when the LLM creates an output that looks valid on the surface level but doesn’t align correctly or does not match the proper semantics of its input query. A typical cause of such hallucinations is having too few subtle inexactnesses between the prompt and how we have encoded words into a model.
Arguably, the simplest type of LLMs are those that represent words as embeddings, meaning they hash a word onto a vector in some high-dimensional space. These embeddings learn semantic relationships between the words based on how they appear in trained data. Sometimes, the vectors are such that the model will respond to things incorrectly in semantic ways when vector spaces and wrong arithmetic create associations between concepts.
For example, when the same term “apple” is embedded in the context of technology and apple fruit separately, there should be a subtle difference between them so that the model can distinguish this. However, if it cannot determine the one context of a word from the other, it may generate a semantically incorrect response. It is a feature that seems to be little understood, especially when it comes to homonyms or figures of speech.
Example
If asked, “What is the best programming language for beginners?” the model might respond, “Python, because it’s an easy reptile to work with,” confusing the semantic context of “Python.”
Mitigation Strategy
Improving the network’s ability to incorporate context and better word disambiguation is needed to combat semantic hallucination. Multi-modal learning techniques allow models to learn from visual, textual, and audio data to better understand word semantics.
III Why Do LLMs Hallucinate?
The reasons behind LLM hallucinations are rooted in how these models are designed and trained. Let’s explore why they occur:
Probabilistic Nature of LLMs
Training Data Limitations
Lack of Real-Time Data Access
Ambiguity in Prompts and User Inputs
Lack of True Comprehension and Reasoning
Model Overconfidence and Output Calibration
1. Probabilistic Nature of LLMs
LLMs use probabilistic modeling to predict the word/phrase most likely to be next after a given input. These are models that, at their core and for the most part, are just trying to predict a token (i.e., word) in a sequence based on patterns learned from their training data. Hallucinations happen because of the dependence on probability rather than factual verification.
LLMs such as GPT-4 are built on transformer architectures and employ self-attention mechanisms to assign importance scores for each word in the input context. While this architecture is suitable for complex language understanding and generation, it does not inherently have any mechanism to verify whether the information is correct or incorrect. LLMs, instead, rely on statistical likelihoods based on training data.
During text generation, the model considers all possible next words and assigns a probability for each based on patterns learned from the training data. If we prompt the model with ”Albert Einstein is known for the theory of…,” the model is highly likely to generate “relativity” because it has seen this association repeatedly in its training data. However, if faced with a less familiar or ambiguous context, the model may “guess” the next word based on statistical correlations rather than factual knowledge, leading to hallucinations.
In 2023, OpenAI’s study revealed that sometimes GPT-4 makes up facts, giving inputs based on probabilistic “guessing” in areas where the model had low-confidence knowledge. It is more likely to happen in niche topics or when the user asks about never occurring or rare events not covered well enough by the model’s training data.
2. Training Data Limitations
Large language models are built using extensive, though limited, datasets that pull text from numerous sources like websites, books, forums, and more. Though not exhaustive, these datasets may contain biases, outdated information, or gaps. This could make the model trip up and give you wrong information, as the model must generate responses even with incomplete or inaccurate information.
LLMs are pre-trained on static datasets, meaning their knowledge is limited to the data they were exposed to during training. As a result, two significant problems arise. The first is the lack of real-time data access, and the second is the inherent biases in the training datasets.
2.a Data Gaps
LLMs cannot access real-time information unless explicitly connected to a live data source. Therefore, any events, developments, or discoveries that occurred after the model’s training cutoff date are unknown to the model, leading to potential hallucinations.
If you are trying to get information about recent events, you may be disappointed. Models trained only up to 2020 just couldn’t provide any insights about what happened in 2021 or later.
2.b Bias in Training Data
Another factor that influences the model’s accuracy is the bias present in the training data. The datasets used for training may not always be diverse or balanced, which can skew the model’s results. If the model’s training data over-represents certain viewpoints or sources, it may hallucinate content based on biases in the data.
A language model trained predominantly on English-language news articles may hallucinate facts when asked about non-Western history, as the relevant information may be underrepresented in its dataset.
Interestingly, GPT-3.5 and GPT-4 trained on English-centric datasets hallucinate more frequently when needed to answer questions about non-English cultures, languages, or historical events. This shows just how much training data bias can affect the accuracy of models, especially when dealing with global issues.
3. Lack of Real-Time Data Access
Most language models are pre-trained and don’t have the ability to access real-time data from the internet or other live sources, which prevents them from updating their knowledge on the fly. Because they can’t tap into live data, models often hallucinate, especially when asked about recent news or advanced topics. This is a major issue.
As seen with many language models, they are trained in batches, meaning they learn from huge but unchanging datasets. After the training phase, they cannot access external APIs, search engines, or databases unless specifically integrated into a system that supports such capabilities. So when you use a language model, it’s stuck relying on the information it learned during training, which gets old quickly.
Since they can’t look up real-time info, models try to guess based on what they already know. The problem is that sometimes they’re guessing with old information that no longer works. When it comes to fast-changing areas like medicine or tech, these models often get it wrong. The inability to pull live data worsens the problem when people ask about what’s happening now.
For example, If someone asks a model about the most recent COVID-19 strain or a 2023 vaccine, it might make up something or offer old details from before the pandemic evolved.
A study by Stanford University in 2022 showed that pre-trained models without real-time data access hallucinated 17% of queries related to current events. On the other hand, giving models real-time data access makes a huge difference. The error rate drops to just a few percent when they can get up-to-date info.
4. Ambiguity in Prompts and User Inputs
Large language models are very responsive to the way questions or prompts are worded. If a user inputs something unclear or incomplete, the model might misunderstand the meaning and fill in the blanks, which can result in hallucinations.
Models like GPT rely on something called token embeddings to handle and analyze user input. Essentially, each word or phrase is transformed into a vector in a large, multi-dimensional space, and the model then prioritizes these tokens using self-attention. It decides which ones matter more within the sentence. But when the question isn’t clear or leaves much room for interpretation, the model can easily get confused about the correct meaning.
This becomes a big issue with words that have multiple meanings, like homonyms, or when someone asks a vague question. The model has to guess what you really mean. These models don’t actually reason like humans do; they just follow patterns in the data. So, when the question isn’t clear, the model makes guesses, sometimes leading to incorrect answers.
5. Lack of True Comprehension and Reasoning
Even though language models are quite advanced at creating text, they don’t really understand language like people do. Because they don’t truly understand, LLMs can’t reason through the information they generate, nor can they check whether their responses make sense logically.
These models work by predicting what word should come next based on patterns. The model generates text by processing input through layers of attention mechanisms, but it does so without “understanding” the meaning or implications of the text it produces.
This becomes a bigger issue when the model has to handle something complex, like solving multi-step problems or keeping track of multiple details. In such cases, LLMs can generate coherent-sounding but fundamentally flawed or inconsistent responses, leading to hallucinations.
For example, if someone asks what happens when you mix bleach and ammonia, the model could give the wrong answer because it doesn’t understand the danger involved in that reaction.
6. Model Overconfidence and Output Calibration
Language models tend to give answers with a lot of confidence, even when they aren’t sure if they’re right. This is called overconfidence—models sound sure of themselves, even when they don’t have the facts to back it up.
Models get overconfident because they’re trained to focus on sounding smooth and coherent. That’s how they’re built. When training these models, the main idea is to make them sound just like a person would. But because they’re so focused on being fluent, they sometimes give answers that sound way more certain than they should be.
LLMs do not have built-in mechanisms for output calibration. This means they don’t actually have a way to adjust how sure they are about something. That’s why you can get responses that seem totally certain, even when they’re not based on facts.
IV Consequences of LLMs Hallucinations
LLM hallucinations aren’t simply tech errors; they can lead to real-life problems, including:
Trust issues
Misleading information in critical fields
legal and ethical concerns
impact on AI adoption
Here’s why it matters: trust. When people run into hallucinations, particularly in critical fields like finance or healthcare, they may lose trust in the AI system. And trust is essential for AI adoption. Once it’s lost, people become hesitant.
For example, a financial advisor who relies on an AI tool that spits out wrong market predictions could make bad investment calls. Or take healthcare—an AI-powered medical assistant might suggest the wrong treatment or diagnosis, which could have severe consequences.
These hallucinations also raise tricky legal and ethical questions. Who’s responsible when an AI system gives wrong or harmful information? The user? The developer? Or the company deploying it? This needs to be sorted out as AI keeps growing in society.
If businesses see too many hallucinations, they might start backing away from AI altogether, slowing innovation.
For example, a company considering using an AI chatbot might reconsider if that bot gives wrong or confusing answers too often.
of LLMs Hallucinations
Trust issues
Misleading information in critical fields
Legal and ethical concerns
Impact on AI adoption
V LLM Hallucination Examples
Many well-known language models have shown signs of hallucinations. Below are some real-world examples of how these models can go off-track:
1. ChatGPT Hallucination
ChatGPT is used by millions, but even it can fall victim to hallucinations. At one point, the model confidently came up with a fake reference to a study that didn’t actually exist when asked a science-related question.
For example, there were many cases when users asked ChatGPT about studies on how exercise affects mental health. It even made up a ‘2020 study by Dr. John Smith from Harvard,’ but no one by that name has done any research like that.
2. BERT Hallucination
BERT, another popular LLM, has been found to hallucinate in natural language understanding tasks. In some cases, BERT misunderstood the context of a question, leading to inaccurate responses.
When asked, ‘Is apple a fruit or a company?’ BERT gets it wrong and only says it is a company, totally ignoring that ‘apple’ has two meanings.
3. Industry-Specific Examples
It’s not just general models that make these mistakes. Even industry-specific ones, like those used in healthcare or finance, aren’t safe from hallucinations.
For example, an AI-powered medical tool was asked about treatments for a specific rare condition, and it provided a fabricated treatment plan that didn’t exist in any medical literature. In finance, an AI model designed to help analysts predict market trends gave a report full of wrong data and confusing conclusions.
| LLM Model | Hallucination Type | Example |
|---|---|---|
| ChatGPT | Factual Hallucination | Provided fake scientific study references |
| BERT | Contextual Hallucination | Misunderstood the dual meaning of “apple” as both a fruit and a company |
| Healthcare LLM | Factual Hallucination | Suggested a non-existent treatment plan for a rare medical condition |
| Finance LLM | Logical Hallucination | Generated false correlations in a financial report |
VI LLM Hallucination Detection: How to Spot Them
Cross-Referencing FactsVerify information with reliable sources, especially specific facts, figures, or references, to ensure accuracy.
Fact-Checking Tools Use tools like Google Fact Check Explorer or FactMata to verify information automatically and in real time.
Spotting Logical Gaps Check if the response aligns with the question; unrelated or illogical answers suggest hallucinations.
Spotting Logical Gaps Check if the response aligns with the question; unrelated or illogical answers suggest hallucinations.
Spotting LLM hallucinations can be tricky, but there are a few ways to catch them before they lead to problems.
1. Cross-Referencing Facts
One of the easiest ways to catch a hallucination is to double-check the information with a trustworthy source. This is especially important when the LLM provides specific facts, figures, or references. For instance, if the model gives you a study or report as a reference, it’s worth checking if it actually exists and if the details line up.
2. Fact-Checking Tools
BERT, another popular LLM, has been found to hallucinate in natural language understanding tasks. In some cases, BERT misunderstood the context of a question, leading to inaccurate responses.
When asked, ‘Is apple a fruit or a company?’ BERT gets it wrong and only says it is a company, totally ignoring that ‘apple’ has two meanings.
3. Monitoring Unusual Confidences
Sometimes, language models sound too sure of themselves, even when wrong. If you notice the model giving really specific details without saying ‘maybe’ or ‘I think,’ that’s a red flag that it might be making stuff up.
4. Spotting Logical Gaps
Another way to spot hallucinations is by checking the logic in the response. If the answer doesn’t follow from the question or jumps to unrelated topics, it’s probably a hallucination. For example, if you ask about the causes of climate change and the model starts talking about space exploration, that’s a clear sign it’s off track.
VII How to Prevent LLM Hallucinations
Preventing LLM hallucinations requires a combination of better model training, human oversight, and responsible use of the technology. Here’s how we can reduce the risk:
1. Improved Training Techniques: Larger, More Accurate, and Diverse Datasets
To prevent LLMs from making stuff up, we need better training, human supervision, and smarter use of the technology. One of the best ways to reduce hallucinations is using larger, more accurate, and diverse datasets during training. Hallucinations usually happen when models don’t have enough info on a topic or when the data they were trained on is biased or incomplete.
Models like GPT-4 are trained on massive datasets without much human input, so they learn from all kinds of data. While this gives them a wide range of knowledge, it also means that the quality of the data can vary. If the data is missing important details or is biased, the model might end up making things up when it tries to generate responses on topics it doesn’t know much about.
A good way to fix this is to train LLMs with specialized, high-quality data in specific fields. For instance, an LLM used in healthcare could be trained using peer-reviewed medical journals or reliable databases like PubMed. This type of focused training can make the model’s responses more accurate and reduce the chance of hallucinations.
2. Real-Time Data Access and Integration
LLMs tend to make mistakes when they don’t have access to the latest or live information. They’re usually trained on older data, meaning they miss out on any events or research that happened after their last update. This can lead to hallucinations, especially when they’re asked about current news or new trends.
Even the data cutoff date of the last update of ChatGPT, GPT-4o, is January 2022. This is almost a two-year gap. Most of the time, LLMs rely on old data and can’t pull in real-time info. But if we connect them to live data sources like APIs or databases, they could give more accurate answers instead of making things up.
Setting up systems that allow LLMs to get real-time data from trusted sources can greatly reduce hallucinations. For example, when you ask an LLM about a recent election or a new scientific discovery, it can check live sources instead of guessing based on outdated info.
3. Human-in-the-Loop (HITL) Systems: Combining Human Oversight with AI
One way to avoid hallucinations in important areas is by using a human-in-the-loop (HITL) system. This means having humans involved in the AI’s process to catch mistakes like hallucinations before they reach the user. In HITL systems, LLMs generate the initial output, but human experts step in to review it, especially in areas like healthcare, law, or finance, where accuracy is crucial.
For industries where getting it right is essential, HITL systems can be used to check all AI-generated work. This is especially helpful in creating content, drafting legal documents, or assisting with medical diagnoses, where having human oversight can stop wrong information from causing problems.
For example, in a legal setting, an LLM might draft a legal document based on a lawyer’s input, but before the document is finalized, a human lawyer reviews it to catch any hallucinations or misapplied legal references.
Improve Decision-Making for Business Growth
4. Confidence Scoring and Calibration: Identifying Low-Confidence Responses
You can make LLMs more reliable by adding systems that check how confident the model is in its answers. Confidence scoring is when the model gives each response a score, showing how sure it is about the answer. This helps users spot answers that might be wrong so they can double-check them.
The model gives a confidence score for each word it uses based on how often it’s seen that word in similar situations during training. If the model isn’t very confident in its answer, it lets users know they might want to check the information. This is really helpful in places where even small mistakes could cause big problems.
Low-confidence responses can be flagged for review or backed up with more data. In customer support chatbots, for example, if the model’s not sure about its answer, it could recommend talking to a real person or checking the info against a reliable source.
5. Prompt Engineering: Crafting Better Queries
A lot of hallucinations happen when prompts are vague or unclear. LLMs are really sensitive to how prompts are phrased, so if a question is too open-ended, the model might make things up. Prompt engineering is about asking clear, specific questions that help the model answer more accurately. But by asking more specific questions, you can guide the model and reduce mistakes.
For example, instead of asking, ‘What happened in May of 2023?’, a better question would be, ‘What were the biggest advancements in AI in May of 2023?’ That way, the model clearly knows what you’re asking. Indeed, using more explicit prompts reduces hallucinations, especially when dealing with complex questions.
6. Reinforcement Learning from Human Feedback (RLHF): Optimizing Model Outputs
Reinforcement Learning from Human Feedback (RLHF) is a new approach to help reduce hallucinations by improving how LLMs create content. A study in 2023 found that using RLHF with OpenAI’s GPT-4 cut factual errors by more than half compared to models that didn’t use human feedback. With RLHF, human reviewers rate how accurate and useful the model’s answers are, and the input is used to fine-tune the model to focus on being correct, not just sounding smooth.
The process involves the model generating answers and human reviewers checking them for accuracy, logic, and usefulness. The model then uses this feedback to improve and give more accurate answers. RLHF is especially helpful in fields like law, finance, or medicine, where getting the facts right is really important.
7. Multimodal Learning: Using Multiple Data Types to Improve Context
Multimodal learning is a new area of research where models are trained to handle different types of data simultaneously, like text, images, and audio. By combining these different kinds of data, LLMs can get a better sense of context, which helps cut down on hallucinations, especially when the text isn’t clear.
Training LLMs to use both text and visual data can reduce hallucinations caused by misunderstandings or missing context. For example, if the model is asked about the Eiffel Tower’s architecture, it can check its answer against a picture of the tower to make sure the description is accurate.
Just like AI software must be free from hallucination, it’s also critical that it’s secure enough to withstand any cyber attack. To learn more about AI cyber security, follow the link to read our blog.
LLM Hallucinations
Dos
Improved Training Techniques
Real-Time Data Access and Integration
Human-in-the-Loop (HITL) Systems
Confidence Scoring and Calibration
Prompt Engineering
Multimodal Learning
Reinforcement Learning from Human Feedback (RLHF)
To Sum Up
LLM hallucinations are more than just tech hiccups—they can lead to misinformation, bad decisions, and people losing trust in AI. Whether you’re in healthcare, finance, or customer service, relying on incorrect AI outputs can cause serious problems. That’s why stopping LLM hallucinations should be a top concern for anyone using AI.
By understanding why these hallucinations happen and how to prevent them—whether with better training, real-time data, or human oversight—you can make your AI more trustworthy. The goal is to ensure your AI provides reliable information, especially in critical fields.
If you want to stop hallucinations in your AI or need advice on improving its accuracy, contact our AI experts. We’re here to help make sure your AI is smarter and more reliable.
