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        <title>LLM as a Broken Telephone: Iterative Generation Distorts Information</title>
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        <namePart type="given">Amr</namePart>
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    <name type="personal">
        <namePart type="given">Michalis</namePart>
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            <namePart type="given">Mohammad</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T LLM as a Broken Telephone: Iterative Generation Distorts Information
%A Mohamed, Amr
%A Geng, Mingmeng
%A Vazirgiannis, Michalis
%A Shang, Guokan
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F mohamed-etal-2025-llm
%X 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.
%R 10.18653/v1/2025.acl-long.371
%U https://aclanthology.org/2025.acl-long.371/
%U https://doi.org/10.18653/v1/2025.acl-long.371
%P 7493-7509

Markdown (Informal)

[LLM as a Broken Telephone: Iterative Generation Distorts Information](https://aclanthology.org/2025.acl-long.371/) (Mohamed et al., ACL 2025)

• LLM as a Broken Telephone: Iterative Generation Distorts Information (Mohamed et al., ACL 2025)

ACL

• Amr Mohamed, Mingmeng Geng, Michalis Vazirgiannis, and Guokan Shang. 2025. LLM as a Broken Telephone: Iterative Generation Distorts Information. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7493–7509, Vienna, Austria. Association for Computational Linguistics.