Solvability of Hallucinations in Large Language Models

Determine whether the hallucination phenomenon observed in large language models can be resolved, or whether it is an inherent limitation of current large language model architectures and training paradigms.

Background

The paper emphasizes that hallucinations—confidently stated but incorrect outputs—are a pervasive limitation of current LLMs, with documented instances such as fabricated citations. While newer systems reduce the rate of such errors via mitigation strategies, the authors note that the underlying mechanisms of hallucination may stem from the fundamental design of LLMs as probabilistic next-token predictors rather than systems grounded in factual knowledge.

This raises a core research question about whether hallucinations can be eliminated through algorithmic or architectural advances, or whether they are intrinsic to the statistical nature of present-day LLMs.