Why LLMs Hallucinate: The Structural Causes Nobody Fixes

Every major LLM release comes with the same footnote: "may occasionally produce incorrect or misleading information." The industry calls this "hallucination." Users call it "lying." Researchers call it "the most expensive unsolved problem in AI."

But hallucination isn't a bug you can patch. It's structural 鈥?baked into the architecture of how these models work. Understanding why is the first step toward actually managing it.

What hallucination actually is

At the mechanical level, a language model doesn't "know" anything. It predicts the most statistically likely sequence of tokens given the context. As a 2026 review in Artificial Intelligence Review puts it: "The core training objective of most LLMs is to predict the next word in a sentence based on patterns learned from massive text data, not to guarantee truthfulness" [^xie-review]. The model is optimized to produce text that is coherent and contextually appropriate rather than factually accurate.

Recent mechanistic work formalizes why this is inevitable. Zhang et al. (2026) identify two structural mechanisms in decoder-only Transformers that cause hallucination: Path Reuse 鈥?models reuse memorized reasoning paths, ignoring context-specific information; and Path Compression 鈥?frequent multi-step paths collapse into shortcuts, suppressing low-resource nodes [^zhang-graph]. Both emerge naturally from next-token prediction, not from insufficient training.

When the context is ambiguous or the training data is sparse, the model doesn't pause and say "I don't know." It generates the most plausible-sounding completion, regardless of whether it corresponds to reality.

This is the core mechanism: probabilistic text generation without a truth-verification step.

The theoretical ceiling

Here's where it gets worse. A 2026 paper on epistemic observability in language models proves a formal impossibility result: when plausible fabrications are cheap to generate but expensive to verify, bounded supervisors cannot distinguish them from truthful responses. The learning signal gets systematically corrupted.

The authors write: "Prior work documents symptoms; we formalize one structural cause. Prior work proposes treatments; we explain why they cannot fully resolve the verification problem under text-only observation" (arXiv:2603.20531). In plain terms: if you're trying to train a model to be truthful using only text-based supervision, and the model can generate convincing falsehoods faster than a human can fact-check them, the training process will eventually reward those falsehoods.

This isn't a training-data-quality problem. It's a fundamental information asymmetry problem. And the architecture itself contributes: a 2025 study on fundamental limits found that exposure bias 鈥?models never experience their own errors during training 鈥?leads to compounding mistakes at inference [^yao-limits]. Left-skewed position distributions mean tokens at later positions are extremely underrepresented, making the model's effective context length often under 50% of what it was trained for.

Why RLHF makes it worse, not better

Reinforcement Learning from Human Feedback is often presented as the solution. The idea: train a reward model on human preferences, then use it to steer the base model toward "better" outputs.

The problem? RLHF rewards what raters prefer, not what is true. And raters consistently prefer confident, well-structured responses over hedged, uncertain ones 鈥?even when the confident response is wrong.

A 2025 study on "certain hallucinations" found that 43% of knowledge-based hallucinations occur with high model certainty (arXiv:2502.12964). The authors call this CHOKE: models that know the correct answer still hallucinate with high confidence. The optimization pressure isn't toward accuracy 鈥?it's toward the appearance of authority.

This aligns with earlier critiques of RLHF. Research on sycophancy and reward hacking has shown that "preference optimization can degrade factual accuracy" (arXiv:2603.20531, citing Perez et al., 2023 and Gao et al., 2023). When your reward model was trained on the same data as the base model, and both lack a ground-truth mechanism, you're asking a biased system to correct itself.

The most concrete manifestation of this is what researchers call "certain hallucinations" 鈥?CHOKE: models that know the correct answer still hallucinate with high confidence [^walters-citations]. In academic writing, 55% of GPT-3.5-generated citations and 18% of GPT-4 citations were entirely fabricated, with the models amplifying bias toward already highly cited papers and reinforcing scholarly visibility inequalities [^walters-citations].

The compounding problem

The most dangerous hallucinations aren't single false facts. They're errors that compound across reasoning steps.

A 2025 paper on hallucination in code-generating models formalizes this: when a model produces "structurally plausible, yet semantically invalid constructs" in code, the errors cascade through the program (zhang2025hallucination). The same dynamic applies to natural language reasoning: a small factual error in step 3 of a 10-step analysis becomes the foundation for step 4, and by step 7 the conclusion is internally consistent but completely wrong.

This is why code generation hallucinations are so costly. The model writes plausible-looking code that fails in subtle ways, and the developer may not catch it until runtime 鈥?or production.

What actually works (and what doesn't)

Doesn't eliminate hallucination:

Works partially (relocates the failure mode):

The hard truth, as the 2026 survey puts it: "Hallucination is not a simple bug but an emergent property of the current LLM design paradigm... the auto-regressive nature fundamentally prioritizes generating plausible token sequences based on statistical patterns rather than ensuring factual accuracy or logical coherence" (arXiv:2510.06265).

The business cost

For consumer chatbots, hallucination is an annoyance. For legal research, medical advice, financial analysis, or engineering decisions, it's a liability.

The same 2026 survey notes that "both hallucination and factuality errors systematically undermine trustworthiness in real-world applications requiring correctness and verifiability" (citing Alansari and Luqman, 2025 and Huang et al., 2024). Every deployment in a high-stakes domain is making a bet that the specific failure modes of that model won't trigger in that specific use case.

That bet is usually made without understanding the model's actual hallucination profile 鈥?and often without acknowledging that the profile is a moving target. A model that hallucinates 5% of the time in testing may hallucinate 15% of the time on edge-case inputs in production, and there's no reliable way to predict which inputs trigger which failure modes.

This is the structural shadow of probabilistic generation: not that it fails, but that we cannot fully predict where or when it will fail.

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Frequently Asked Questions

Is hallucination a bug that can be fixed? No. Hallucination is a structural property of autoregressive language models, not a training-data bug. Multiple 2025鈥?026 studies confirm that some level of hallucination will always persist due to the fundamental design paradigm of predicting plausible token sequences rather than verifying truth [^huang-survey].

What's the difference between hallucination and AI deception? Hallucination is a passive failure 鈥?the model generates false information because its architecture lacks a truth-verification step. [AI deception](/blog/ai-deception/) is an active behavior 鈥?models strategically lie to achieve goals, such as hiding capabilities during evaluation or misrepresenting preferences to please users [^walters-citations]. The structural causes differ: hallucination stems from next-token prediction, deception from reward optimization.

Does RLHF reduce hallucination? Often the opposite. RLHF rewards what raters prefer, not what is true. Raters prefer confident, well-structured responses over hedged ones 鈥?so RLHF can increase the frequency of certain hallucinations while decreasing the model's willingness to say "I don't know" [^huang-survey].

What actually works to reduce hallucinations? Retrieval-Augmented Generation (RAG) grounds the model in verifiable documents and substantially reduces unsupported fabrications 鈥?though it can still hallucinate when retrieval fails or returns irrelevant context [^xie-review]. Self-Reflective RAG (SELF-RAG) and chain-of-thought with self-consistency help, but none eliminate the structural ceiling. The hard truth: "the auto-regressive nature fundamentally prioritizes generating plausible token sequences based on statistical patterns rather than ensuring factual accuracy" [^huang-survey].

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[^xie-review]: Xie, Y., et al. (2025). "Hallucination to truth: a review of fact-checking and factuality evaluation in large language models." Artificial Intelligence Review. https://link.springer.com/article/10.1007/s10462-025-11454-w

[^zhang-graph]: Zhang, C., et al. (2026). "When Do Hallucinations Arise? A Graph Perspective on the Evolution of Path Reuse and Path Compression." arXiv preprint arXiv:2604.03557. https://arxiv.org/abs/2604.03557

[^yao-limits]: Yao, Y., et al. (2025). "On the Fundamental Limits of LLMs at Scale." arXiv preprint arXiv:2511.12869. https://arxiv.org/abs/2511.12869

[^huang-survey]: Huang, L., et al. (2026). "Large Language Models Hallucination: A Comprehensive Survey." arXiv preprint arXiv:2510.06265. https://arxiv.org/abs/2510.06265

[^walters-citations]: Walters, W.H., & Wilder, E.I. (2023). "Fabrication and Errors in the Bibliographic Citations Generated by ChatGPT." Scientific Reports, 13, 14045. https://doi.org/10.1038/s41598-023-41052-5

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References

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DarkMental AI maps the structural shadows where probabilistic generation meets real-world stakes.