Understanding Extrinsic Hallucinations in Large Language Models: Causes and Mitigation

What Are Hallucinations in Large Language Models?

When we talk about hallucination in the context of large language models (LLMs), we usually refer to instances where the model produces content that is unfaithful, fabricated, inconsistent, or simply nonsensical. Over time, this term has been stretched to cover nearly any mistake the model makes. However, a more precise definition is needed to address the underlying issues effectively.

In this article, we narrow the discussion to cases where the model’s output is fabricated—meaning it is not grounded in either the provided context or widely accepted world knowledge. This refined focus allows us to explore practical solutions without conflating different types of errors.

In-Context Hallucination vs. Extrinsic Hallucination

Hallucinations can be divided into two primary categories:

• In-context hallucination: The model’s response should be consistent with the source content given in the input context. When it invents details that contradict that context, it is an in-context hallucination.
• Extrinsic hallucination: The model’s response should be grounded by its pre-training dataset. Since that dataset (serving as a proxy for world knowledge) is enormous, verifying every fact against it is too expensive. In practice, we expect the output to be factual and verifiable by external world knowledge.

This article focuses on extrinsic hallucination, the more challenging type to detect and prevent.

The Challenge of Extrinsic Hallucination

Extrinsic hallucination arises when the model generates information that has no basis in its training data—or that contradicts well-established facts. Because the pre‑training corpus is vast and often contains conflicting information, simply retrieving relevant knowledge for every generation is computationally prohibitive. As a result, the model may confidently produce statements that are completely made up.

Consider a scenario where an LLM claims a historical event happened on a certain date, but that date is not supported by any authoritative source in its training set. That is an extrinsic hallucination. The model does not “know” it is wrong; it simply generates plausible-sounding text.

Why Pre-Training Data Matters

The pre‑training dataset serves as the model’s internal “knowledge base.” If that dataset is biased, incomplete, or contains inaccuracies, the model is more likely to hallucinate. Moreover, because the dataset is static, the model cannot update its knowledge after training—it may not know about recent events unless they were included in the training data. This limitation is a root cause of many extrinsic hallucinations.

Mitigating this requires better training data curation and techniques that help the model recognize when a given fact is outside its knowledge.

Reducing Extrinsic Hallucinations: Factuality and Uncertainty Acknowledgment

To avoid extrinsic hallucination, LLMs need two key capabilities: factuality and acknowledgment of uncertainty. These are not independent; a model that can honestly say “I don’t know” when it lacks reliable information is less likely to fabricate answers.

Ensuring Factual Output

Factuality can be improved by:

1. Retrieval-augmented generation (RAG): The model first retrieves relevant documents from a trusted knowledge base, then generates an answer grounded in those documents. This reduces reliance on the model’s internal memory.
2. Fine-tuning on factual datasets: Training on curated sets of verified facts helps the model learn to produce accurate information.
3. Constrained decoding: Techniques that force the model to follow specific templates or avoid certain types of statements can reduce hallucination risk.

Acknowledging Uncertainty

Equally important is teaching the model to admit when it does not know the answer. Approaches include:

• Confidence calibration: Training the model to output a confidence score with each answer so that low-confidence predictions can be flagged or rejected.
• Explicit “I don’t know” fine-tuning: Including examples in the training data where the appropriate response is to state uncertainty.
• External verification: Using a separate tool or fact‑checker to validate the model’s claims before presenting them to the user.

Combining these strategies creates a system that is both more accurate and more trustworthy, reducing the frequency and impact of extrinsic hallucinations.

Conclusion

Extrinsic hallucination remains one of the most difficult problems in LLM deployment. By distinguishing it from in‑context hallucination, we can design targeted solutions that address the root cause: the model’s reliance on a static, imperfect training corpus. Fostering both factuality and the willingness to express uncertainty will be critical for building reliable AI assistants. For more on related techniques, see the section above.