Not All Hallucinations Are Hallucinations: Context Compression and Misinterpretation in LLM Interactions

Large Language Models (LLMs) are often criticized for producing hallucinations, broadly understood as fabricated or incorrect outputs. However, this paper argues that a substantial portion of reported hallucinations are misattributions rather than genuine model-generated falsehoods. This work systematically disentangles multiple failure modes that are frequently conflated under the term hallucination, including question-design-induced artifacts, silent context-loss-induced omissions caused by context compression, and reconstruction artifacts arising from probabilistic compensation. A central claim is that by the time users detect anomalous behavior, the relevant context has often already become irrecoverable, making post-hoc clarification or summarization inherently incomplete. Through an extended interactive case study, the paper introduces the High-Functioning Compensation Effect, a phenomenon in which high linguistic fluency and inferential capability delay anomaly detection by seamlessly masking missing information. Paradoxically, higher output quality can therefore increase the likelihood of hallucination misattribution, reinforcing user distrust despite structurally consistent model behavior. Rather than framing hallucination as a purely model-centric failure, this paper reframes the issue as a contextual and UX-driven misinterpretation problem. Practical usage guidelines are proposed, emphasizing early externalization of core assumptions and proactive context management, and treating LLMs as volatile processors rather than persistent memory systems. This work aims to clarify responsibility, improve evaluation practices, and support more reliable and transparent human–LLM interaction.