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LLM Hallucinations — Causes and Remedies

Redaktion ·

LLM Hallucinations — Causes and Remedies

A hallucination is a fluent, confidently phrased statement from a large language model (LLM) that is simply false or freely invented. The tricky part: it sounds just as convincing as a correct answer. That’s exactly why hallucinations are the biggest trust problem in the practical use of LLMs — and the reason you should never take model output at face value on important topics. The compact term is in the glossary under hallucination; this article explains causes, types, and above all the remedies.

Why LLMs hallucinate

The core lies in how they work: an LLM predicts the most probable next token, not the true one. It optimizes for plausible continuations, not for factual accuracy. Between “sounds plausible” and “is correct” there’s a gap — and hallucinations live in that gap.

Several factors amplify it:

  • Knowledge gaps. On topics barely present in training (niches, local details, very recent events), the model invents plausible-sounding gap-fillers.
  • Training cutoff. Knowledge ends at a fixed date. What happened after is unknown to the model — but it guesses anyway.
  • Ambiguous prompts. Unclear or underspecified instructions give the model room to guess in the wrong direction.
  • Training incentives. Current research argues that common training and evaluation procedures reward guessing over acknowledging uncertainty — a confident guess scores better on a test than an honest “I don’t know” (OpenAI, 2025).

Types of hallucinations

Not every hallucination looks the same. Three patterns show up especially often in practice:

Factually wrong. The model asserts a concrete, checkable fact that isn’t true — a wrong date, an invented number, a swapped attribution.

Invented sources and citations. Especially insidious: the model “backs up” a claim with a study, a legal paragraph, or a URL that never existed. The reference looks real but doesn’t exist.

Instruction drift. The model deviates from the instruction — answers a different question, ignores a constraint, or mixes contexts. With long prompts that fill the context window, it more easily loses the thread and fills gaps with invention.

Remedies that actually help

There’s no single switch. What works is a combination of layers — the higher the risk, the more of them.

RAG / grounding. The strongest measure: instead of letting the model answer from memory, you supply relevant, vetted documents as context. The model should quote or paraphrase from these sources, not generate freely. How this works is covered in detail in understanding RAG. Grounding shifts the answer from “what is probable” to “what’s in the source”.

Source enforcement. Require in the prompt that every claim is backed by a source from the supplied context — and that the model explicitly says “not contained in the sources” when it can’t find something. This makes invented evidence visible.

Determinism for facts. Where the model allows it, a low randomness setting (such as a low temperature) reduces variance and thus creative drift — useful for factual, not creative tasks. Note: some current models no longer expose this control; then you steer via clear prompts and a verification pass.

Verification / second pass. Have a claim checked against the sources in a second step — by the model itself with a strict checking prompt, or by a separate control. Whatever the first pass asserts, the second must be able to back up.

Clear, unambiguous prompts. The more precise the instruction, the less room to guess. Specify format, scope, and limits, and explicitly state what to do when uncertain.

Human-in-the-loop for high risk. For legal, medical, or financial statements, a human belongs in the loop. A fail-closed design helps: if confidence falls below a threshold, the system escalates to a human rather than guessing confidently.

FAQ

What is a hallucination in an LLM? A fluent, convincingly phrased model output that is factually wrong or freely invented. The problem is the confidence: hallucinations sound exactly like correct answers and are hard to spot without checking.

Why do LLMs hallucinate at all? Because they predict the most probable next token, not the true one. Add to that knowledge gaps, a training cutoff, and training incentives that reward confident guessing over admitting uncertainty.

Can hallucinations be eliminated entirely? No. They are an inherent feature of probabilistic models; accuracy never reaches 100 %. But they can be substantially reduced through grounding, source enforcement, verification, and human review.

What’s the single most effective remedy? RAG, or grounding: supplying the model vetted sources as context and having it quote from them rather than answer from memory. This shifts the answer from “plausible” to “backed up”.

Why are invented sources especially dangerous? Because they fake trust. An invented study or a non-existent paragraph looks like evidence but isn’t. That’s why every cited source should be checked against the context that was actually supplied.

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