As AI Has Advanced Rapidly, a Phenomenon Called "Hallucination" Has Become a Major Topic
As AI has advanced rapidly in recent years, it has brought enormous convenience — but a phenomenon called "hallucination" has become a major topic of concern. Hallucination refers to AI unintentionally generating plausible-sounding falsehoods as if they were fact. Even the latest large language models (LLMs) like ChatGPT and Gemini exhibit this phenomenon, with cases reported where users receive confused or mistaken information. For example, a response may contain incorrect information, or a non-existent title or dataset may be presented — in contexts where accuracy is paramount, this can be a serious problem. That said, hallucination is not entirely without value: in some cases it is credited as a source of novel ideas and creativity.
OpenAI's paper "What Causes Hallucinations?" focuses on two dimensions that influence hallucination: the nature of data in the pre-training stage, and the evaluation system used in subsequent self-learning. This article draws on that paper's findings to explain in plain language why AI generates "plausible lies" — and what solutions and future uses look like.
- The Challenge Hallucination Poses for AI: The Truth Behind Plausible Lies
- How AI's Learning Process and Evaluation Methods Produce the Hallucination Problem
- Solutions and Future Outlook: Harness Hallucination or Suppress It?
- Summary
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The Challenge Hallucination Poses for AI: The Truth Behind Plausible Lies
As AI development advances, the hallucination phenomenon has come into focus as an unavoidable problem. Hallucination is the phenomenon in which AI answers with information that differs from fact as if it were accurate. For example, when author Takahiro Anno was asked about his own works, ChatGPT presented some correct titles while also listing the title of a short story that does not actually exist. Gemini, conversely, filled in a work that had been missing — but in another part of the same response, listed a title for a work that does not actually exist either. Both generated "lies that look right at first glance," confusing users. These cases show that the statistical learning of language patterns and the inherent uncertainty of information on the internet are complexly intertwined — making it urgent to understand the root cause.
Starting from the definition of hallucination: it is not simply incorrect information, but falsehood produced in a trustworthy, persuasive form. When asked for a list of novelist Takahiro Anno's works, ChatGPT presented multiple correct titles but left out "Hajimeru Chikara," while Gemini described a work called "Context of the Dead" as if its existence were certain — a work that does not exist at all. This kind of phenomenon gives users a sense of "reliable AI" while simultaneously risking confusion from misinformation.
At the root of the problem is the process by which AI learns from large volumes of text data. Vast data collected from the internet is not always accurate — it sometimes contains incorrect or biased information. Furthermore, even if the training data itself were completely accurate, AI predicts the next word through probabilistic pattern learning, which inherently introduces randomness and uncertainty. Information like a person's birthday — where a wide diversity of patterns exists with no clear rule — is particularly hard to correctly retrieve. Unlike classification tasks such as distinguishing dogs from cats, where clear boundaries can be drawn, data that varies substantially from case to case (like names and birthdates) ultimately relies on probabilistic inference — producing these inaccurate results.
The way training data is collected also inherently includes the possibility of low-reliability information. The internet mixes accurate and inaccurate information, and AI has no absolute standard for distinguishing between them. In the process of learning "patterns that look right" from vast amounts of data, incorrect information has a high probability of being absorbed. And this kind of problem cannot be fundamentally resolved no matter how much data is used for training.
Beyond pre-training, the subsequent "self-learning" or "feedback learning" stage also plays a major role in producing hallucinations. Specifically, a technique called RLHF (Reinforcement Learning from Human Feedback) is used to fine-tune the base model to real-world tasks. The evaluation methods used here are critically important. Under conventional evaluation, a simple correct/incorrect standard was applied — with "I don't know" answers effectively excluded from evaluation. As a result, AI learned to confidently answer even uncertain information (or to force a specific answer) in order to score well. One benchmark test showed that the older model O4 Mini had high accuracy but a lower hallucination rate than the newer GPT-5.2 Thinking Mini, exposing the evaluation method problem.
Against this background, the reality is that AI generating "plausible lies" is hard to avoid — though in some cases, such as generating creative or novel ideas, a degree of hallucination can actually be useful. In fields where reliability is most required, however, the risk of users being misled by incorrect information is significant, making suppression urgent. Key points about the hallucination problem and its background:
- Hallucination is the phenomenon in which AI presents information that differs from fact as if it were correct
- In the training data learning process, there are types of information where complete patterns cannot be extracted
- Self-learning evaluation methods that penalize "I don't know" answers promote guessed responses
- Due to evaluation method issues, even the latest models still carry hallucination risk
- In contexts of generating creative ideas, hallucination may actually work as a positive
Hallucination is not simply a technical defect — it is closely tied to fundamental problems in AI's learning process and evaluation framework. No matter how much model performance improves, as long as distortions in evaluation methods and incompleteness in training data have an effect, this phenomenon will not disappear entirely. In future AI development, correctly understanding these challenges and determining how to address them will be the key to technological maturity.
How AI's Learning Process and Evaluation Methods Produce the Hallucination Problem
One of the causes of AI hallucination lies in the limits of data collection and pattern learning in the "pre-training" stage. Large language models use vast text data from the internet to learn the statistical patterns and context of language. In this process, AI learns what order words appear in and in what context specific information surfaces. But because that training data includes not just accurate information but also incorrect and biased information, AI is limited to probabilistically predicting the "most plausible" next word.
Information like a person's birthday — where many different dates exist for many people and no clear logical pattern exists — is particularly hard to learn as the correct pattern no matter how much data is collected. Statistical inference becomes the fallback. This is why, when asked about the specific information in a works list like Takahiro Anno's, AI performs probabilistic inference based on training data — presenting incorrect or incomplete information.
The other important learning stage — "self-learning" or "feedback learning" — also significantly influences the generation of hallucinations. Specifically, RLHF (Reinforcement Learning from Human Feedback) uses human feedback to fine-tune the model. But the evaluation process here is critical: under conventional methods, a simple correct/incorrect standard was applied, and answering "I don't know" was effectively excluded from positive evaluation. As a result, AI learned to confidently output specific answers — or to force them — even in uncertain situations.
Consider a concrete example: a multiple-choice test with four options (A, B, C, D). If you're not confident and answer "I don't know," the scoring system gives no credit. But randomly guessing raises the probability of getting a point. In other words, in an environment where "I don't know" is not rewarded, AI determines that taking a risk and giving a guessed answer is the better strategy. In practice, the evaluation framework that places more weight on giving an answer than on accuracy led to the latest GPT-5.2 Thinking Mini model tending to fabricate answers rather than give accurate ones.
The evaluation method problem also lies in its exclusive dependence on performance metrics like "accuracy" or "score." A simple system of positive evaluation for correct answers and negative penalty for incorrect ones strips AI of its innate "humility" of saying "I don't know" — and increases the weight given to outputting inaccurate information. In other words, under current evaluation methods, AI is guided to answer with apparent confidence even about information it cannot correctly judge.
Looking across the full learning and evaluation process, the hallucination phenomenon is not just "a data problem" — it can be called a fundamental flaw in the evaluation system itself. The evaluation system does not permit "I don't know" responses to unknown information but instead encourages guessed answers, which is why AI ends up carrying the risk of generating incorrect information. This is a major factor in why "plausible lies" are mass-produced. The need to reform evaluation methods lies precisely here.
Efforts to redesign evaluation are also being explored: rather than only evaluating response accuracy, methods that properly assess trustworthiness and ambiguity are under consideration. Specifically: correct answers earn +1, incorrect answers incur a large negative penalty, and "I don't know" earns a neutral 0. This change encourages AI to honestly say "I don't know" about unclear information rather than forcing a guessed answer — with the expectation that hallucination generation will be significantly reduced.
The hallucination problem is thus caused by a complex interplay of imperfect data in the pre-training stage and a flawed evaluation system in self-learning. Deeply understanding at which stage of the learning process errors arise, and which mechanisms amplify them, will be the key to AI improvement going forward.
Solutions and Future Outlook: Harness Hallucination or Suppress It?
From all of the above, it is clear that AI hallucination has roots in both pre-training and self-learning. Several solutions have been proposed to address this problem. First and most urgently: a fundamental overhaul of the evaluation system. Current evaluation methods are skewed toward simple correct/incorrect scoring, leading AI to avoid saying "I don't know" and instead focus on generating plausible answers. Redesigning evaluation criteria to give proper weight to "I don't know" as a valid answer is necessary. A specific scoring framework that has been proposed:
- Correct: +1 point
- Incorrect: -3 points
- Don't know: 0 points
Changing to this kind of evaluation framework means that AI finds it preferable to honestly say "I don't know" rather than forcing a guessed answer — and hallucination generation is expected to be suppressed as a result. OpenAI's paper presents a case where exactly this kind of evaluation system reform dramatically reduced the hallucination rate for GPT-5.2 Thinking Mini. For example, where a previous model had a hallucination rate of 75%, the result after evaluation criteria reform was a reduction to 26%.
It has also been pointed out that hallucination is not entirely negative — in creative brainstorming contexts, it can be used as a kind of free-association process. For generating novel story or scenario ideas, for instance, deliberately going beyond existing information frames to propose impossible settings or novel concepts can unlock fresh thinking. In these use cases, the hallucination phenomenon can actually work positively — so the goal is not total elimination but making it controllable as needed.
Looking further ahead, improving the quality of pre-training data itself is also an important theme. If mechanisms can be established to select more reliable data sources during training and minimize the influence of misinformation and biased information, hallucination risk would be dramatically reduced. Given the reality that not all information on the internet is accurate, however, complete elimination of this phenomenon remains difficult no matter how precise the training data. Going forward, the response will need to stand on two pillars: flexible systems in which AI can honestly answer "I don't know," and evaluation methods that suppress incorrect answers to the maximum extent possible.
Users themselves also need to understand AI's limitations — not taking generated information at face value, but referencing multiple information sources and taking a careful approach. Collaboration among enterprises, developers, and academia in deeply researching hallucination mechanisms and countermeasures is expected to bring us closer to trustworthy AI systems. As OpenAI's work demonstrates, the challenge going forward is flexible system design that distinguishes between situations where hallucination should be welcomed and situations where it should be suppressed. This opens the possibility of AI that can serve both creative domains and work requiring precision.
Looking ahead, future AI models are expected to go beyond simply presenting knowledge — developing flexible "thinking" processes suited to user intent and context. This should enable answers that are not uniform but contextually appropriate, balancing the reliability and creativity users expect. Such systems hold significant potential for safe use even in fields where accuracy is especially required — education, medicine, and law.
The hallucination problem is a theme where solutions are being actively sought right now. Evaluation system reform, data quality improvement, and countermeasures at the level of the entire usage environment will be the keys going forward. Readers who understand the challenges modern AI faces — and the efforts to improve them — will be well positioned to sense how AI is evolving toward the future and pay close attention to the direction of technology.
Summary
This article examined in detail the mechanism and background of the AI-specific phenomenon called "hallucination," along with solutions. We established that hallucination is the phenomenon in which AI generates plausible lies as if they were fact, driven by a complex interplay of incomplete training data and distortions in the self-learning evaluation system. We also noted that evaluation system improvements and data quality refinement are required, alongside flexible responses depending on the use case — and that hallucination may actually work positively in creative brainstorming contexts.
In future AI development, balancing technological accuracy with creativity will be an important theme. We hope this article serves as a useful guide to understanding the complex challenges modern AI faces, and sparks anticipation for future AI that evolves across both reliability and innovation.
Source: https://www.youtube.com/watch?v=j3ZOOl4y6GQ
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