Hallucinations as Orthogonal Noise: Inference-Time Manifold Alignment via Dynamic Contextual Orthogonalization
arXiv:2606.03022v1 Announce Type: new Abstract: Hallucination in Large Language Models (LLMs), characterized by the generation of content inconsistent with contextual facts or logical constraints -- remains a persistent challenge for reliable deployment. In this work, we address this issue through a geometric framework rooted in the linear representation hypothesis. We propose that hallucinations manifest as orthogonal noise relative to the semantic manifold of the residual stream. Specifically, we hypothesize that while attention heads ideally propagate information congruent with the context
This research addresses a critical and persistent challenge in Large Language Models (LLMs) as their deployment becomes more widespread and reliability becomes paramount.
Improving LLM reliability by mitigating hallucinations is crucial for their commercial adoption, especially in sensitive applications, impacting trust and utility.
This geometric framework offers a new theoretical understanding and potential inference-time solution for LLM hallucinations, which could lead to more robust and trustworthy AI systems.
- · AI developers
- · LLM-dependent industries
- · AI safety researchers
- · Enterprise AI users
- · Companies relying on unreliable LLM outputs
- · Current hallucination mitigation techniques
Reduced hallucination rates in LLMs lead to more trustworthy and effective AI applications.
Increased adoption of LLMs in high-stakes domains due to enhanced reliability and explainability.
Accelerated development of general AI agents as the core foundation becomes more robust and predictable.
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Read at arXiv cs.CL