Hallucination Causes › Generation Pressure

Even if a model had perfectly accurate training data and no exposure bias, the generation process itself introduces pressures that favor fluent hallucination over honest uncertainty. The generation process is, fundamentally, a sequence of probability distributions: at each step, the model assigns a probability to every token in its vocabulary and selects one. Nothing in this process encodes "I don't know." The only question is which token the model generates; in the absence of reliable knowledge, it generates a plausible-sounding one.

Language models are trained to always continue a sequence. The training objective rewards producing probable continuations; there is no built-in mechanism for "stopping because I don't know" or for outputting a distinguished "uncertain" token. If a question implies that a factual answer exists, the model generates the most statistically plausible answer, not "I am not sure."

This is fundamentally different from a retrieval system, which can return zero results when no relevant document is found. A language model's probability distribution over vocabulary always has a mode, and the model will always produce the mode (or a sample near it). There is no probability mass on "abstain." The generation process is complete only when a termination token is produced or a length limit is reached. Abstention is not a natural outcome; it is a learned behavior that must be explicitly trained.

Instruction-tuning can teach models to express uncertainty with phrases like "I'm not certain, but..." or "I don't have reliable information about that." But this is learned as a surface pattern rather than a calibrated epistemic state. The model learns to prefix uncertain-sounding outputs with uncertainty qualifiers while still generating the most probable factual claim, which may or may not be accurate. The uncertainty language is decorative unless it is backed by a reliable mechanism for detecting when the model is operating outside its knowledge boundary.

This matters because the form of a question creates what can be called completion pressure: a strong signal about what type of answer is expected. A well-formed factual question creates an expectation of a factual answer. The model, having learned from millions of question-answer pairs where questions reliably receive detailed answers, is under structural pressure to produce a detailed answer even when no reliable answer is available. The path of least resistance is to generate a plausible-sounding response rather than to acknowledge ignorance, because the former produces lower perplexity under the distribution of question-answer pairs in training.

The web is largely written by people who are confident in what they say. Academic papers state conclusions directly. News articles report facts declaratively. Forum responses answer questions authoritatively. Technical documentation makes definitive claims. The model learns the rhetorical style of confident, fluent prose because that is what the training data predominantly contains.

This means the model learns a prior in favor of confident assertion. Hedged, uncertain language ("it is unclear whether," "some sources suggest," "I am not certain") appears far less frequently in training data than confident declarations. As a result, the model's default generation mode is confident, not hedged, independent of whether it has reliable knowledge to back up that confidence.

The effect is reinforced by the structure of instruction tuning and RLHF annotation. Human annotators rating responses tend to prefer responses that sound knowledgeable and direct over responses that sound uncertain and hedged, all else being equal. This annotator preference, encoded into the reward model used for RLHF, pushes the model further toward confident generation. The very feedback process meant to align the model with human preferences can inadvertently train it to be overconfident, because confidence is often mistaken for competence in evaluating text quality.

If a question's phrasing strongly implies that a specific type of answer exists, the model generates an answer of that type even without reliable knowledge. "Who invented X?" implies a person's name is the answer. "When was X established?" implies a year. "What is the capital of Y?" implies a city. The model is sensitive to these pragmatic implications because it has seen millions of question-answer pairs in training, and question-answer co-occurrence creates strong prompt-completion associations.

This means that a factual question about a subject the model does not know can still receive a confident, structured, plausible-sounding answer, because the question's format telegraphs exactly what kind of answer is expected. The model knows the answer's form; it does not know the answer's content; it fills the form with the most statistically plausible content given the question's subject.

The practical implication is that the model is better thought of as an extremely sophisticated pattern matcher than as a reliable oracle. For high-frequency questions with well-established answers, the pattern matching points to the right answer. For rare questions with uncertain or unknown answers, the pattern matching produces an answer of the right form but wrong content.

The specific decoding strategy used at inference time affects hallucination rates in measurable ways. Each strategy makes different tradeoffs between fluency, diversity, and factual reliability.

Greedy decoding (always selecting the argmax token) tends to produce locally optimal but globally inconsistent outputs. Each token is individually the most probable given context, but this greedy choice may commit to a factual direction that becomes increasingly inconsistent as the sequence grows. Greedy decoding also tends to be repetitive and can get stuck in loops where the most probable next token keeps reinforcing the same context.

Top-$p$ (nucleus) sampling selects the next token by sampling from the smallest vocabulary subset whose cumulative probability exceeds a threshold $p$. Higher $p$ values allow more randomness, which increases creative diversity but also increases the probability of selecting factually deviant tokens. Lower $p$ values reduce diversity but push generation toward the most probable tokens, which may reinforce confident errors rather than preventing them.

Temperature scaling modifies the token probability distribution before sampling. For a vocabulary distribution with logits $\mathbf{z}$, temperature $\tau$ transforms the softmax as:

$$P(x_i) = \frac{\exp(z_i / \tau)}{\sum_j \exp(z_j / \tau)}$$

where:

• $P(x_i)$: the probability assigned to token $i$ after temperature scaling
• $z_i$: the unnormalized logit for token $i$ from the model
• $\tau$: the temperature parameter (positive real number)
• The sum in the denominator is over all tokens $j$ in the vocabulary

When $\tau < 1$, the distribution sharpens toward the most probable tokens, reducing diversity and sometimes reducing certain types of hallucination. When $\tau > 1$, the distribution flattens, increasing diversity at the cost of more random sampling that may deviate from factual content. Neither direction reliably eliminates hallucination: sharpening can reinforce confident factual errors, and flattening can introduce new ones.

Beam search maintains multiple candidate sequences in parallel, selecting the globally highest-probability continuation over the full beam. While this typically improves coherence compared to greedy decoding, it does not improve factual accuracy: it finds the most probable hallucinated story as effectively as the most probable factual one. Beam search may produce more coherent hallucinations, because the beam selection pressure favors internally consistent sequences over correct but locally less probable ones.