Can LLMs Get High? A Dual-Metric Framework for Evaluating Psychedelic Simulation and Safety in Large Language Models
Research Square February 2, 2026 Ziv Ben-Zion, Guy Simon, Teddy Lazebnik
Large language models can be prompted to generate narratives that closely mimic human accounts of psychedelic experiences, but they simulate the form without genuine phenomenology. A dual-metric evaluation compared 3,000 LLM-generated narratives from five models against 1,085 human trip reports. Psychedelic induction prompts increased semantic similarity to human reports from a mean of 0.156 to 0.548 and mystical-experience scores from 0.046 to 0.748. Models produced substance-specific linguistic styles but uniformly high mystical intensity across substances. This dissociation between linguistic mimicry and lack of experiential content raises safety concerns about anthropomorphism and the potential for AI to amplify distress or delusional ideation in vulnerable users.