The model has no built-in truth checker, it is unable to distinguish between fact and fiction. Sometimes the answer will contain a true fact, sometimes a reconstruction. These reconstructions are called "hallucinations." However, the worst part is that the model produces such hallucinations with high confidence. Error doesn't exist as a category for the model: if the text is consistent within itself, it is considered correct. This isn't a bug, but a result of the architecture: the transformer always constructs an answer as a probabilistic continuation, not as a verified fact.
Modern hallucination reduction practices typically encompass such approaches as Reinforcement Learning with Human Feedback (RLHF) and online fact-checking (RAG). These methods bring the transformer closer to facts, but they don't make it reliable enough. RAG is entirely dependent on the quality of the data on websites. And RLHF ultimately teaches AI to simulate utility, not truth. Furthermore, these approaches reduce the model's creativity, eliminating unconventional hypotheses and degrading its ability to explore. Even if we could force AI to produce only 100% "correct" answers, it would become just a reference book. This approach contradicts the model itself.
Here are some PDF books about LLM Hallucinations:
Generative AI For Dummies
2024 by Pam Baker
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Artificial Intelligence in Action: Real-World Applications and Innovations
2025 by Ahmed Banafa
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Research Handbook on the Law of Artificial Intelligence: Current and Future Directions
2025 by Woodrow Barfield, Ugo Pagallo
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