title: LLM as a Broken Telephone: Iterative Generation Distorts Information
summary: 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.
paper_authors._id: 67ca7b557436e6327ca877ff
paper_authors.name: Amr Mohamed
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-07T09:09:41.298Z
paper_authors._id: 655efd24afee0e00788bb589
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/655efd24afee0e00788bb589/22guLxIWNybbJR3jI-c4w.jpeg
paper_authors.fullname: Amr Mohamed
paper_authors.type: user
paper_authors.user: amr-mohamed
paper_authors._id: 67ca7b557436e6327ca87800
paper_authors.name: Mingmeng Geng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-07T09:34:23.443Z
paper_authors._id: 67890323f8796231c857231e
paper_authors.avatarUrl: /avatars/f5ccd5186968d880fee9c36324a5f713.svg
paper_authors.fullname: Mingmeng Geng
paper_authors.type: user
paper_authors.user: mgeng
paper_authors._id: 67ca7b557436e6327ca87801
paper_authors.name: Michalis Vazirgiannis
paper_authors._id: 67ca7b557436e6327ca87802
paper_authors.name: Guokan Shang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-07T09:09:38.933Z
paper_authors._id: 6087e598e2b7cc3a117b0dc5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6087e598e2b7cc3a117b0dc5/Ctz_W-uo1gOQRBHXalD1P.png
paper_authors.fullname: Guokan Shang
paper_authors.type: user
paper_authors.user: guokan-shang
paper_title: LLM as a Broken Telephone: Iterative Generation Distorts Information
paper_summary: 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.
paper_ai_keywords: large language models iteration translation-based experiments information distortion language choice chain complexity strategic prompting techniques