Why LLMs Hallucinate on Structured Knowledge: A Mechanistic Analysis of Reasoning over Linearized Representations
arXiv:2605.26362v1 Announce Type: new Abstract: In many reasoning tasks, large language models (LLMs) rely on structured external knowledge, such as graphs and tables, which is typically linearized into sequential token representations. However, even when sufficient knowledge is available, LLMs can still produce hallucinated outputs, and the underlying mechanisms behind such failures remain poorly understood. We investigate these mechanisms and find that hallucinations arise from systematic internal dynamics rather than random noise. First, attention disproportionately concentrates toward shor
This research provides a timely and mechanistic understanding of LLM hallucination, a critical and current limitation hindering wider adoption and reliability of AI systems.
Understanding the systematic causes of LLM hallucinations is crucial for developing more reliable AI, which directly impacts the integrity of automated reasoning and decision-making.
The focus for improving LLM reliability shifts from broad architectural changes towards targeted interventions addressing specific internal dynamics, such as attention mechanisms operating on linearized structured data.
- · AI researchers focusing on explainability
- · Developers of robust LLM applications
- · Industries requiring high-integrity AI output
- · LLM developers ignoring mechanistic analysis
- · Applications relying on unvalidated LLM outputs
Improvements in LLM training and architecture design will be directly informed by these mechanistic insights, leading to more reliable models.
Reduced hallucination rates will accelerate the integration of LLMs into critical sectors requiring high factual accuracy, such as finance and legal domains.
Increased trustworthiness could expand the scope of autonomous AI applications, but also raise new questions about accountability when systematic failures persist.
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Read at arXiv cs.CL