Understanding Why Language Models Hallucinate: Testing Reasoning Against Priors

2026-07-01

AI Summary

This work addresses the tendency of large language models to hallucinate when adhering to prompt constraints, arguing that such failures are often misattributed to knowledge gaps but actually stem from a misalignment between the model’s reasoning pathways and its prior knowledge. The study formally characterizes this reasoning misalignment mechanism, identifying two failure modes: task retrieval bias and critical choice bias. It proposes a latent-variable critical-task model, demonstrating that frequency imbalances during pretraining lead models to favor statistically prominent shortcut paths over constraint-sensitive reasoning trajectories. Through a controlled diagnostic framework, TrapQA, and two curated constrained question-answering datasets—ScientistQA and Real-Life Constrained QA—the experiments substantiate that hallucinations predominantly arise from biased latent reasoning processes, offering a novel conceptual and methodological foundation for understanding and mitigating such errors.

Abstract

Large language models often produce hallucinated answers that violate prompt-level constraints. A key diagnostic question is whether these failures reflect missing knowledge, or whether the model has the relevant information but follows the wrong inference path. We study this phenomenon as inference misalignment: a mismatch between the answer supported by the prompt and the answer favored by statistically salient latent associations. We formalize this view with a latent key-task model, in which pretraining-frequency imbalance can cause a shortcut path to dominate the constraint-sensitive path and induce positive inference loss. The framework predicts two failure modes: task-retrieval bias in entity disambiguation and key-selection bias in action choice. We introduce TrapQA, a controlled diagnostic testbed with two components. ScientistQA tests disambiguation among similar scientists with supplementary factual probes, while Real-Life Constrained QA tests everyday constraint following under salient shortcuts. Our results show that hallucination can arise from biased latent inference rather than absent knowledge alone.

Problem

• hallucination
• inference misalignment
• language models
• reasoning
• latent associations

Innovation

• inference misalignment
• latent key-task model
• hallucination
• shortcut reasoning
• TrapQA