LLM as a Broken Telephone: Iterative Generation Distorts Information
Authors: name: Amr Mohamed, name: Mingmeng Geng, name: Michalis Vazirgiannis, name: Guokan Shang

As large language models are increasingly responsible for online content,
concerns arise about the impact of repeatedly processing their own outputs.
Inspired by the "broken telephone" effect in chained human communication, this
study investigates whether LLMs similarly distort information through iterative
generation. Through translation-based experiments, we find that distortion
accumulates over time, influenced by language choice and chain complexity.
While degradation is inevitable, it can be mitigated through strategic
prompting techniques. These findings contribute to discussions on the long-term
effects of AI-mediated information propagation, raising important questions
about the reliability of LLM-generated content in iterative workflows.