From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data
arXiv:2606.07537v1 Announce Type: cross Abstract: Large language models hallucinate--producing fluent, confident, factually wrong outputs--with a consistency that persists across generations and scales. Existing taxonomies classify hallucination by output type, distinguishing intrinsic from extrinsic failures and faithfulness from factuality divergence. These frameworks are descriptively rigorous but do not identify which internal mechanism produced a given instance. This paper analyses hallucination as a structural consequence of three architectural decisions that together form a compound fai
The paper provides a foundational analysis of LLM hallucination, shifting the focus from descriptive classification to identifying internal architectural causes, which is crucial as LLM deployment scales.
Understanding the structural origins of hallucination is critical for developing more reliable and trustworthy AI systems, impacting their commercial viability and public adoption.
The focus for mitigating LLM hallucination will likely shift more towards fundamental architectural adjustments and data curation, rather than solely post-hoc error correction.
- · AI researchers focused on foundational model architecture
- · Companies developing robust, verifiable AI systems
- · Users and industries relying on factual AI outputs
- · AI models with unaddressed architectural vulnerabilities
- · Applications where factual accuracy is paramount but not prioritized in model de
- · Companies relying on superficial fixes for LLM hallucination
Improved understanding of LLM limitations and new research directions for fundamental improvements.
Development of next-generation LLM architectures designed from the ground up to reduce inherent hallucination.
Increased trust in AI systems leading to broader integration into critical applications, contingent on successful mitigation strategies.
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Read at arXiv cs.LG