Large language models, often called LLMs, have changed how artificial intelligence communicates. They let computers write in a way that feels almost human, which explains why chatbots and virtual assistants have become so common. Even with all that progress, these systems have a serious weakness: sometimes they make things up. In AI research, that mistake is called a hallucination. It happens when the model gives an answer that sounds sure of itself but turns out to be wrong. Most of the time, the issue goes back to what the model learned from. If the data is biased, incomplete, or uneven, the model tries to fill in the blanks by guessing. Because it predicts patterns instead of truly understanding meaning, it often produces answers that look believable at first but don’t hold up. Hallucinations can take many shapes: a wrong fact, mixed-up logic, or a reply that skips the context entirely. A small error might not matter much in casual conversation, but in places like hospitals or courtrooms, a single mistake can cause real harm. As these systems become part of everyday life, the real work lies in figuring out why they slip and how to stop it from happening so often. Only by tackling that can we build AI tools that users feel they can rely on and trust.

Understanding hallucination in Large Language Models

What are the main causes of hallucination in LLMs?

Hallucinations in large language models happen because of the mix between how the model is built, what it learns from, and the way it produces answers. A key reason behind this issue is that LLMs depend heavily on patterns found in their training data. When those links are stretched too far, the model can create text that seems reasonable but is actually wrong or made up.[1] Because the system predicts words based on what usually follows, it sometimes forms sentences that look correct but have no real connection to facts or verified information. This shows how weak or mistaken word connections can directly cause hallucinations.[2]

Another problem comes from the kind of knowledge the model sees most often while learning. Frequent ideas tend to drown out rarer ones, so the model repeats what it has seen too often and ends up giving answers that sound narrow or biased.[3] In real use, hallucinations show up in two main forms: the model either misremembers what it learned or invents something entirely new. Both the data itself and the way the model builds its replies share responsibility for these mistakes.[4] The way these elements interact makes it clear that focused solutions are needed to limit hallucinations. Efforts such as improving how models link to real information, refining how they connect ideas, and representing knowledge more accurately can all help reduce hallucinations and make LLM outputs more trustworthy.

How does hallucination manifest in generated outputs?

Hallucinations usually show up as repeated false information, like invented names or made-up details, which can appear again and again when the model is asked the same question. This repetition is not just coincidence. Research shows that nearly half of these false results come back when the same prompt is used again, and many reappear multiple times after a few attempts.[5] Around sixty percent of the invented items resurface within ten prompts, showing that hallucinations are not random errors but patterns the model tends to repeat.[6]

Because these mistakes happen in predictable ways, they can open the door to security problems. Hackers or other bad actors could take advantage of these predictable errors by creating harmful content with the same names as the hallucinated ones, tricking users into trusting or installing them.[7] This overlap between technical flaws, safety risks, and user trust shows just how damaging hallucinations can become. Since these errors keep showing up, they make people lose confidence in language models and leave systems more vulnerable to abuse. For this reason, strong detection and prevention tools are needed to catch and reduce hallucinations, protect the quality of generated content, and keep users safe.

How do training data and model architecture contribute to hallucination?

Hallucinations appear when the model’s structure and its training data influence each other in the wrong way. When the training data is unbalanced, and some topics appear more often than others, the model becomes focused on the topic it has seen most. This phenomenon of “knowledge overshadowing” makes those patterns dominate the answers. Thus raising the chance of hallucinations.[8] The bigger the gap between common and rare examples in the data, the stronger this effect becomes, showing that uneven training data directly increases the risk of hallucinations.[9]

When some topics are explained in greater length and detail than others, the model tends to overgeneralize and produce answers that sound right but lack real evidence.[10] Errors and vague statements in the training data make this problem even worse. The model learns from these mistakes and may create smooth, natural-sounding text that isn’t actually based on accurate information, increasing the chance of hallucination.[11] Although the data is a big part of the issue, the model’s own design also plays a role in how hallucinations form. When a model is repeatedly trained on uneven or messy data, it starts to overgeneralize, and that behavior comes from how the system itself is built.[12] To fix hallucinations, researchers need to work on both sides: cleaning and balancing the data while also designing models that can spot and control the overuse of certain patterns. This difficult problem shows why teams must act early during data preparation and model design to stop recurring hallucinations before they can be exploited for harmful purposes.

Trust and reliability concerns in Large Language Models

How does hallucination affect user trust in LLM-generated content?

Hallucinations make it hard to tell what is real and what is not, and that uncertainty causes people to lose trust in the model’s answers. This loss of trust affects how people use technology and whether they feel comfortable depending on it.[13] When an LLM gives a response that sounds convincing but is wrong, users might believe it and even pass it along to others. Once those mistakes are noticed, people’s confidence in both the specific answer and the system overall usually falls.[14] The problem gets worse because the model often writes in a confident, authoritative tone, even when it is wrong. That tone makes it harder for users to judge what is true and what is false, which spreads misinformation and further damages trust.[15]

All these issues are linked, showing that when LLMs spread wrong information, it not only hurts confidence in digital tools but can also affect daily choices and make people slower to accept AI. To tackle this, developers should focus on making AI systems clearer, labeling what is factual, and giving people simple ways to verify what they read. Rebuilding trust depends on helping users tell the difference between genuine information and generated text so they can make informed choices.

What strategies exist to detect and mitigate hallucinations in LLMs?

Researchers are testing several ways to reduce hallucinations in LLMs. Some methods use outside knowledge sources, others have the model check its own work, and some look inside the model to study how it handles information. A major approach is connecting the model to structured databases, like knowledge graphs, which give it reliable factual context to work from. This method helps keep its answers tied to reality and reduces the chances of producing false but believable statements.[16] Studies comparing different methods show that linking models to external data improves factual accuracy but also exposes the limits of these current fixes. This shows that more work is needed to find the best ways to use outside information effectively.[17]

Another promising idea is to let models rate how confident they are before answering, using techniques like self-evaluation or self-familiarity. That kind of system acts as a safety check, letting the model skip or rethink questions it is unsure about, which helps prevent many hallucinations.[18] Other research focuses on studying what happens inside the model, at the token or representation level, to detect when it starts to go off track and to correct those errors before they appear in the output.[19]

There isn’t one perfect solution. Combining outside data sources, internal checks, and careful monitoring works best. When applied together, these methods make the model’s responses far more reliable and trustworthy.[20] In the future, improving and combining these approaches will be key to building dependable and understandable LLM systems, especially in areas where accuracy truly matters.

How do trust issues impact the adoption of LLMs in critical domains?

The repeated and predictable nature of hallucinations makes organizations hesitant to rely on LLMs in sensitive fields such as healthcare, finance, or public policy, where even small mistakes can have serious results. The problem becomes worse when combined with other challenges like a lack of transparency, weak ethical controls, and the risk of intentional manipulation, all of which make people less confident in using LLMs for critical decisions.[21] For instance, attackers who can predict and exploit repeated hallucinations increase not only technical risks but also the belief that LLMs are unsafe or unreliable for serious tasks.[22]

These trust issues are tied to wider worries about hidden decision-making, social bias, and the overall lack of clarity about how reliable or explainable model outputs really are.[23] Together, these challenges make it clear that strong, proactive measures are needed. Efforts such as building transparent, accountable AI systems, running structured tests, and enforcing strict validation steps can help restore trust and ensure that LLMs are used safely and responsibly in important sectors.[24] If these actions are not taken, ongoing hallucinations and other trust problems will keep creating major barriers to adoption, highlighting the urgent need for joint solutions across disciplines and strong regulatory oversight.

Conclusion

Hallucinations in large language models show the ongoing tension between how fluent these systems sound and how accurate they truly are. The same abilities that allow them to write in a natural, human-like way also make them capable of producing convincing errors. Problems in the data they learn from, combined with design limitations and noise in their training, create responses that can seem logical but are in fact untrue.

The impact goes far beyond simple mistakes. When these errors repeat or appear in important contexts, they can spread misinformation, reduce user confidence, and even pose security risks. The more people rely on LLMs, the greater the need to make their inner workings clearer and their information more dependable. Solving this problem requires a mix of approaches: cleaner and more balanced training data, stronger grounding in verified knowledge, models that can question their own certainty, and systems that make their reasoning easier to understand.

Reducing hallucinations is not simply a technical task; it’s about building models users can trust. Setting clear standards for factual accuracy and explainability will help ensure that these systems are used safely in high-stakes fields. If research continues in this direction, LLMs can move beyond sounding intelligent to being reliable tools that are not only powerful but also transparent, responsible, and genuinely trustworthy.

[1] Sun, Y., Gai, Y., Chen, L., Ravichander, A., Choi, Y. Computer Science > Computation and Language. (n.d.) retrieved September 10, 2025, from arxiv.org/abs/2504.12691.

[2] Ibid.

[3] Zhang, Y., Li, S., Qian, C., Liu, J., Yu, P., Han, C. Computer Science > Computation and Language. (n.d.) retrieved September 10, 2025, from arxiv.org/abs/2502.16143.

[4] Ravichander, A., Ghela, S., Wadden, D., Choi, Y. Computer Science > Computation and Language. (n.d.) retrieved September 10, 2025, from arxiv.org/abs/2501.08292.

[5] Spracklen, J., Jadliwala, M. Package Hallucinations: How LLMs Can Invent Vulnerabilities. (n.d.) retrieved September 10, 2025, from www.usenix.org.

[6] Ibid.

[7] Ibid.

[8] Zhang, Y., Li, S., Liu, J., Yu, P., Fung, Y., Li, J., Li, M. Computer Science > Computation and Language. (n.d.) retrieved September 11, 2025, from arxiv.org/abs/2407.08039.

[9] Ibid.

[10] Ibid.

[11] Filippova, K. Computer Science > Computation and Language. (n.d.) retrieved September 12, 2025, from arxiv.org/abs/2010.05873.

[12] Zhang, Y., Li, S., Liu, J., Yu, P., Fung, Y., Li, J., Li, M. Computer Science > Computation and Language. (n.d.) retrieved September 13, 2025, from arxiv.org/abs/2407.08039.

[13] Hao, G., Wu, J., Pan, Q., Morello, R. Quantifying the uncertainty of LLM hallucination spreading in complex adaptive social networks. (n.d.) retrieved September 13, 2025, from www.nature.com/articles/s41598-024-66708-4.

[14] Ibid.

[15] Ibid.

[16] Agrawal, G., Kumarage, T., Alghamdi, Z., Liu, H. Computer Science > Computation and Language. (n.d.) retrieved September 10, 2025, from arxiv.org/abs/2311.07914.

[17] Ibid.

[18] Luo, J., Xiao, C., Ma, F. Computer Science > Computation and Language. (n.d.) retrieved September 10, 2025, from arxiv.org/abs/2309.02654.

[19] Orgad, H., Toker, M., Gekhman, Z., Reichart, R. Computer Science > Computation and Language. (n.d.) retrieved September 11, 2025, from arxiv.org/abs/2410.02707.

[20] Gumaan, E. Computer Science > Computation and Language. (n.d.) retrieved September 11, 2025, from arxiv.org/abs/2507.22915.

[21] Ferdaus, M., Abdelguerfi, M., Ioup, E., Niles, K. Computer Science > Computers and Society. (n.d.) retrieved September 12, 2025, from arxiv.org/abs/2407.13934.

[22] Mohsin, A., Janicke, H., Wood, A., Sarker, I. Computer Science > Cryptography and Security. (n.d.) retrieved September 12, 2025, from arxiv.org/abs/2406.12513.

[23] Alaharju, H. Ensuring Performance and Reliability in LLM-Based Applications: A Case Study. (n.d.) retrieved September 12, 2025, from aaltodoc.aalto.fi/items/e18a6eba-0586-4ebb-aba0-368bed500162.

[24] Ibid.