Hallucination Causes › Limitations and Impact

Understanding hallucination causes also requires honesty about the limitations of the mechanistic account we have built.

Training data issues, exposure bias, knowledge gaps, and generation pressure are real phenomena, but quantifying their individual contributions to a specific hallucination is not straightforward. When a model generates an incorrect fact, we cannot run a controlled experiment to isolate which cause was responsible. The four mechanisms are entangled throughout training and inference, and their contributions overlap rather than being cleanly separable.

Empirical research has made progress on individual dimensions: studies measuring entity frequency and hallucination rates, analyses of knowledge cutoff effects, and ablations of decoding strategies. But a unified causal model that assigns blame proportionally does not exist. Practitioners must work with incomplete attribution, using the framework of four causes as a diagnostic lens rather than a precise measurement tool.

The measurement problem is particularly acute for training data issues. We generally do not have access to the exact training corpus of any commercial model, and even for open-weight models, corpus composition is often reported at a high level without the detailed provenance information that would allow precise analysis of how errors in specific data sources propagate to specific hallucinations.

Web-scale training means training on web-scale noise. Human curation at the scale of hundreds of billions of tokens is not feasible. Automated filtering can remove low-quality content (boilerplate, spam, obviously AI-generated text) but cannot reliably identify factual errors at scale. There is no practical filter that reads text and checks it against a source of truth for every factual claim.

The fundamental tension between data scale and data quality remains unresolved. Larger models trained on more data tend to hallucinate less on high-frequency facts while remaining vulnerable on tail entities, suggesting that scale helps only where data is already sufficient. For the long tail of entities and facts, more data does not help if the additional data contains similar noise levels to what was already present.

One partial response is to mix web-scraped data with high-quality curated sources (textbooks, encyclopedias, scientific literature) at ratios that give disproportionate weight to verified content relative to its raw volume in the web corpus. This helps but cannot overcome the fundamental problem that high-quality sources cover only a fraction of the world's facts.

Instruction tuning and reinforcement learning from human feedback (RLHF) significantly improve a model's ability to express appropriate uncertainty and abstain from answering when knowledge is insufficient. But they do not retroactively fill knowledge gaps or undo exposure bias at the level of the base model. A model finetuned to say "I'm not sure" more often may produce fewer overconfident hallucinations but still generates factually wrong answers when it does commit to a response.

The causes are encoded in the base model during pretraining. Finetuning modifies the model's response style around those causes without fixing them. This is an important asymmetry: finetuning can make the model appear better calibrated in terms of expressed confidence, but the underlying knowledge gaps and exposure bias patterns remain. If you evaluate only the model's tendency to express uncertainty, finetuning looks effective. If you evaluate whether its stated facts are correct when it does commit to an answer, the picture is less clear.

The practical impact of these causes is asymmetric by application domain. For entertainment and creative writing, hallucination is often acceptable or even desirable: a fictional narrative benefits from imagination, and factual accuracy is not the goal. For medical information, legal research, financial analysis, and factual journalism, the same failures are potentially harmful. A model that confidently fabricates a drug dosage or a legal precedent can cause real harm in ways that a fictional story cannot.

Understanding that hallucination is especially severe for tail entities and post-cutoff knowledge allows product designers to make principled deployment decisions. High-stakes factual queries can be routed to retrieval-augmented systems that ground answers in verified external sources. Fact-checking layers can be applied for sensitive domains. Query types where models are structurally most likely to be wrong (obscure entities, recent events, domain-specific regulations) can be explicitly excluded from model scope or handled with additional verification steps.

The causes framework also informs user interface design. If a deployment involves tasks with high hallucination risk (asking for specific historical facts about minor figures, asking for recent events, asking for detailed technical specifications), communicating appropriate uncertainty to users becomes essential. A user who understands that the model may be unreliable on these specific query types can apply appropriate skepticism; a user who treats all model outputs as equally reliable cannot.