What Is LLM Hallucination And How to Reduce It?
In July 2025, Deloitte delivered an independent review to the Australian government’s Department of Employment and Workplace Relations (DEWR). Shortly thereafter, in August 2025, academic Dr. Christopher Rudge of the University of Sydney exposed the report as being “littered with citation errors.” The list of errors was severe: references to non-existent academic papers, and even a fabricated quote attributed to a Federal Court judge.
Deloitte subsequently confirmed that the Azure OpenAI GPT-4o large language model had been used in the report’s preparation, admitting the errors were due to the phenomenon known as LLM Hallucination. The firm was eventually required to issue a partial refund to the government for the flawed work, turning this incident into a global wake-up call about the dangers of unchecked generative AI in professional settings.
And issues like this mostly happen. If you are not a vigilante you will be misled and you cannot rely 100% on generative AI.
This is more than a technical mistake; it is a fundamental crisis of confidence. Understanding what LLM hallucination is and how to reduce it is now critical for anyone using these tools in high-stakes fields.
Thesis Statement: This article will argue that LLM hallucination is a predictable failure stemming from the models’ probabilistic formation process, where statistical likelihood supersedes verifiable truth. By deconstructing this technical root cause and examining how errors manifest through phenomena like confabulated citations, we can establish a two-pronged strategy for mitigation: embracing Retrieval-Augmented Generation (RAG) as the technical antidote for developers, and adopting rigorous user verification protocols as the essential human defense.
Why LLMs Hallucinate: The Roots of Error (The “Why”)
let’s tackle the toughest question: Why do these powerful AI models mess up and give us false answers?
The short answer is: It’s not a mistake; it’s a design feature. The model is doing exactly what it was trained to do, but its training goal wasn’t truth.
A. The Prediction Machine: Smooth Talker, Poor Fact-Checker
To truly understand this, you have to forget the idea of a database. Your LLM isn’t built like Google Search, which goes out and finds a specific, verified website.
Think of it as a Super-Confident Guessing Game
- Goal: Guess the Next Word: The model’s main job is incredibly simple: it just predicts the most likely word (or chunk of a word) that should come next in a sentence. It learned this by reading billions and billions of examples. This is the Next Token Prediction process.
- The Smoothness Score: It doesn’t get a “Truth Score.” It gets a “Smoothness Score.” Its entire priority is creating output that flows, sounds natural, and is grammatically perfect.
- The Cost of Guessing: Now, imagine you ask the model a question it’s only partially sure about. It finds a statistically “smooth” way to finish the sentence, but since it has no way to check that guess against a verified outside source, it has to invent the missing facts to keep the sentence flowing. It would rather give you a fluent, convincing lie than admit it doesn’t know the full story.
The MIT Confirmation: This is why researchers from MIT are so clear: the models are statistically rewarded for sounding confident even when they are totally unsure. If the training encourages confident guessing, you get confident nonsense.
B. Training Data Contamination: If the Input is Messy, the Output is Messy
To become smart, the model had to learn from the largest “textbook” ever created: the public internet.
What Happens When the AI Reads Everything?
- The Unfiltered Library: Imagine your textbook was compiled by copying almost every single website, forum post, and tweet ever written. This giant collection is incredibly messy! It’s full of biases, outdated information, conspiracy theories, and outright falsehoods.
- A Simple Rule: The model operates under a simple rule: frequency equals importance. If a piece of false information was repeated often enough in its training data, the model registers it as statistically important.
- The Vicious Cycle: When the model repeats this false information, it isn’t “hallucinating;” it’s just accurately reproducing the misinformation it learned from its flawed textbook. This is what we call Data Contamination, and it means the errors are literally “baked in” from the start.
Bias Check: This also explains bias. If negative ideas about a group of people were statistically common in the training material, the model might unfortunately repeat and even emphasize those ideas in its answers, simply because it learned that pattern.
The Mechanism of Error: How Hallucinations Manifest (The “How”)
In the last section, we learned that the model is a very confident guesser. Now, let’s look at the two main ways that confident guessing gets the model into trouble, leading to the kinds of errors we actually see in reports.
A. Confabulated Citations: The Invented Source
This is one of the most dangerous types of error, because it makes a lie look completely trustworthy. It’s what happened in the Deloitte case with the fake judge’s quote.
Why Does the Model Make Up Citations?
- Pattern Recognition: The model learned, from reading millions of academic papers, that serious statements are followed by a reference (like an author’s name and a year: (Smith, 2024)). It knows the pattern is necessary for formal writing.
- The Pressure to Finish: When you ask the model for a factual summary, and it realizes it doesn’t have the exact, verified data in its memory, it panics a little. It needs to keep the sentence flowing smoothly and formally.
- Synthesis (The Invention): To fill that requirement, the model uses its vast memory of names, journals, and titles to stitch together a completely new, fake citation that looks real. It invents the name, the paper title, and the journal because it’s statistically more likely to include a citation than to admit it has no source. The result is a convincing, but entirely false, “Confabulated Citation.”
B. The Static Mind: Knowledge Cutoffs and Outdated Facts
Another major reason for giving false information is surprisingly simple: the model’s brain is frozen in time.
Why Can’t the AI Tell Me What Happened Last Week?
- The Cutoff Date: Think of the model’s training as a massive history class that ended abruptly on a specific day (for example, September 2023). That date is the Knowledge Cutoff.
- A Simple Memory Problem: Any new invention, law, political change, or scientific discovery that happened after that date is completely unknown to the base model.
- The Problem in Practice: If you ask the model about a current event, it doesn’t say, “I don’t know.” Instead, it confidently relies on its outdated memory and gives you information that was true before the cutoff. This is called Temporal Misinformation (mistakes about time). In fast-moving fields like finance or medicine, this can be worse than a hallucination, because the mistake feels like a fact but is simply obsolete.
User-Side Defense: Mitigating Risk and Verifying Output (The “How to Reduce It” – Part 1)
Since we know we can’t completely stop the model from guessing, the most powerful defense we have is changing how we use the tool. We need to become the final layer of defense—the “human-in-the-loop.”
A. The Default Position: Assume It Needs Checking
The biggest mistake you can make is treating the LLM like an encyclopedia. Instead, you need to adopt a simple rule for all high-stakes tasks: Assume the LLM is wrong until you prove it is right.
- Treat Output as a Draft: Never view the text you get back as a finished product, especially if it contains facts, numbers, dates, or legal references. View it as a first draft, a hypothesis, or a summary. This mental shift forces you to take responsibility for checking the facts.
- Establish a Verification Protocol: For any critical information, you must set up a mandatory process. This protocol means every core factual claim must be cross-referenced with at least two trusted, external sources (like official government sites, verified databases, or peer-reviewed journals).
- Action Step: Always ask yourself: “If I put my name on this, do I trust the AI enough, or do I need a human source?”
B. Strategic Prompting for Accuracy
You can actually coach the model to be more honest and accurate by changing how you ask questions.
- Demand Step-by-Step Reasoning: Instead of asking for the final answer immediately, use prompts that force the model to show its work: “Explain your reasoning, step-by-step, before providing the final answer.” If the logic breaks down in the middle, you’ll spot the flaw before you even get to the invented conclusion.
- Explicitly Request Sources: Always include an instruction like: “For every factual claim, provide a corresponding, verifiable URL or source.” While we know the model can invent sources (confabulate), this instruction provides you with a crucial trail of evidence to check. If the URL is dead or the paper doesn’t exist, you know the claim is false.
- Specify Constraints: If you know the model’s Knowledge Cutoff, you can limit its guessing: “Only use information published within the last two years,” or “If you do not know the answer, state ‘I am unsure’ rather than guessing.”
C. The Human-in-the-Loop Imperative
Ultimately, reducing the risk of a high-profile mistake—like the one with Deloitte—comes down to accepting the Human-in-the-Loop model.
- Change the AI’s Role: Recognize that the AI excels at drafting, summarizing, and brainstorming. It is terrible at verification, ethical screening, and final authoritative sign-off.
- Preventing the Domino Effect: The Deloitte example shows that once an LLM hallucination is incorporated and signed off by a human, it gains false authority. You must perform your verification protocol at the very beginning of the process, before the text becomes part of your official work.
Technical Solutions: The Future of Trust (The “How to Reduce It” – Part 2)
If the user’s job is to double-check the answers, the developer’s job is to make the model smarter and safer. The most promising technical solutions focus on one simple idea: Don’t let the model guess!
A. The Gold Standard: Retrieval-Augmented Generation (RAG)
The best way to stop hallucination, especially in high-stakes professional settings, is by implementing RAG, or Retrieval-Augmented Generation.
Think of RAG as Giving the AI a Verified Textbook
- The Problem: The base LLM can only remember the facts it learned before its Knowledge Cutoff (its training date). It doesn’t know anything current or specific to your company.
- The RAG Solution: RAG connects the LLM to a separate, trusted, and up-to-date database—like your company’s official documents, today’s news headlines, or a verified scientific library.
- How it Works Simply:
- Retrieve: When you ask a question, the system first quickly searches the verified database and pulls out the most relevant paragraphs of text.
- Augment: It then gives these verified facts directly to the LLM and says: “Write the answer based ONLY on these facts.”
- Grounding: The LLM is now grounded in reality. It can’t invent a citation or use an outdated law because it’s forced to use the evidence you provided. This is how major companies solve the hallucination problem in their internal AI tools.
B. Advanced Fine-Tuning: Teaching the Model to Say “I Don’t Know”
Beyond RAG, developers are working to improve the model’s core intelligence, teaching it to recognize its own limitations.
- Fact-Based Re-Training: Researchers take small, highly accurate datasets and use them to re-train (or fine-tune) the model. This process increases the model’s priority for factual correctness, making it less likely to risk a guess.
- Teaching Self-Doubt: A key goal is training the model to recognize when its internal confidence score is low. This teaches it to say, “I am unsure,” or “I cannot find a source for that,” instead of making up a lie. This focus on honesty over fluency is a massive area of research and helps prevent the disastrous “confabulated citation” problem.
The case of Deloitte’s report for the Australian government serves as a powerful, and costly, reminder that the current generation of Large Language Models (LLMs) cannot be blindly trusted. As demonstrated throughout this article, the issue of hallucination is not a transient bug but a direct outcome of the models’ probabilistic formation process, which inherently values fluent speech over verifiable fact.
The Shared Responsibility
We have established that the LLM reliability problem requires a dual-pronged solution, integrating both technical fixes and changes in human behavior:
For Developers (The Technical Antidote): The future of trustworthy AI rests on architectures like Retrieval-Augmented Generation (RAG). By forcing the LLM to ground its output in verified, external data sources, RAG moves the model away from statistical guessing and closer to factual accuracy. This is the path to solving the problem at the code level.
For Users (The Human Defense): For anyone operating in a high-stakes environment, the user verification protocol is non-negotiable. This means abandoning the assumption that the LLM is an encyclopedia. Every core claim—every number, every name, every source—must be treated as a hypothesis that requires cross-referencing with external, trusted information.
The Final Imperative
The crisis of confidence created by widespread LLM hallucination will not be solved by better-sounding lies. It will be solved by a commitment to transparency and verifiable evidence. The utility of generative AI is immense, but its power must be paired with constant human vigilance and continuous technical refinement.