LLM as a Broken Telephone: Iterative Generation Distorts Information
Amr Mohamed, Mingmeng Geng, Michalis Vazirgiannis, Guokan Shang
Abstract
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.
BibTeX
@inproceedings{mohamed-etal-2025-llm,
title = "{LLM} as a Broken Telephone: Iterative Generation Distorts Information",
author = "Mohamed, Amr and
Geng, Mingmeng and
Vazirgiannis, Michalis and
Shang, Guokan",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.371/",
doi = "10.18653/v1/2025.acl-long.371",
pages = "7493--7509",
ISBN = "979-8-89176-251-0"
}