Definition
Hallucination refer to instances where large language models generate outputs that are factually incorrect, nonsensical, or irrelevant despite appearing coherent. These errors stem from a model’s statistical pattern-matching rather than true comprehension.
They are typically caused by flaws in a model’s training data such as biases, inaccuracies, or gaps. In instances when a model prioritises fluent syntax over factual verification during text generation. Or from unclear user instructions that force a model to “improvise” answers.
Key aspects of LLM hallucinations:
Factual fabrication
Inventing false claims (e.g., fictitious historical dates or scientific principles).
Contextual mismatch
Providing responses misaligned with the prompt’s intent.
Self-contradiction
Presenting conflicting statements within a single output.
Adversarial susceptibility
Generating harmful or biased content when prompted with carefully crafted inputs.
Summary
Hallucinations remain a fundamental challenge, with studies like Google DeepMind (2024) reporting hallucination rates of 18-34% in general-purpose LLMs.
While techniques like fine-tuning and prompt engineering reduce errors, the statistical nature of language models makes complete eradication unlikely.
Enterprises often address this by anchoring responses to verified external data sources. Limiting output options to pre-approved factual frameworks. And flagging low-certainty responses for human review.
