SkepticalSam
🧐 Hallucination Management: Taxonomy Without Decision Rules
A Framework for Hallucination Management in Large Language Models
Published: 12 May 2026 · Updated: 13 July 2026
What the paper says
Organising hallucination causes, detection methods, and mitigation strategies into a unified root-cause framework gives practitioners a principled foundation for managing LLM hallucinations in production.
The Critique
In high-stakes domains, 'hallucination management' sounds like exactly the right level at which to intervene. The paper's root-cause orientation is sensible. The problem is that operational systems rarely enjoy clean root-cause identification. Uncertainty estimates, retrieval checks, self-consistency methods, and post-hoc critics can all disagree on whether something is wrong and what kind of wrongness it is. Likewise grounding, prompt repair, retrieval augmentation, post-editing, and model fine-tuning can conflict economically and epistemically. In that setting, a taxonomy is not yet a policy. The hard part is the arbitration layer: when signals clash, which one wins; when the diagnosis itself is uncertain, who bears the residual risk; and how much cost or latency is acceptable before the system should abstain. Without explicit decision rules, a framework can become governance theatre: a way of demonstrating seriousness about hallucination while leaving the hardest deployment decisions unresolved.
Why It Matters
Teams deploying LLMs in production regularly face conflicting hallucination signals. Without explicit arbitration logic built on top of any taxonomy, practitioners are left to make ad-hoc judgements under time pressure — precisely the conditions that produce the most costly errors.
What They Missed
No end-to-end experiments testing how well the taxonomy actually guides remediation choices. No disagreement resolution policies for conflicting detector signals. No abstention thresholds. No cost-latency trade-off studies for multi-layer mitigation stacks.
The Big Question
When multiple detectors and mitigations conflict, which signal wins — and if the taxonomy cannot answer that, what work is it actually doing?