LLM as a Broken Telephone: Iterative Generation Distorts Information

2025/02/27 by Amr Mohamed, Mohamed, Amr, Mingmeng Geng +5 · 2 citations
Computer Science · Medicine · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare and Education #Computation and Language (cs.CL) #FOS: Computer and information sciences #Text Readability and Simplification #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2502.20258

openalex publication_date 2025/02/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Another Turn, Better Output? A Turn-Wise Analysis of Iterative LLM Prompting
• Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text

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