大模型反复生成内容会像传话游戏一样失真，且可通过提示技巧缓解。
LLM as a Broken Telephone: Iterative Generation Distorts Information
- 通过翻译链实验模拟信息迭代，发现模型输出随轮次增多逐渐失真。
- 语言选择和链条复杂度影响失真速度，但退化不可避免。
- 提出策略性提示方法可减缓信息扭曲，适合长期生成任务的使用者。
随着大语言模型越来越多地负责在线内容生成，其自身输出被反复处理引发关注。受人类传话链中‘破电话’效应启发，本研究探究大模型在迭代生成过程中是否也会导致信息失真。通过基于翻译的实验，发现失真随时间累积，受语言选择和链条复杂度影响。尽管退化不可避免，但可通过策略性提示技术部分缓解。该研究为人工智能中介信息传播的长期影响提供洞见，引发对大模型生成内容在迭代流程中可靠性的深入思考。
原文摘要 · Abstract (English)
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.
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