研究大模型如何适配不同语言背景用户，提升沟通效果。
How desirable is alignment between LLMs and linguistically diverse human users?
- 分析大模型在年龄、性别、多语经验等差异下的语言适应策略。
- 指出语言多样性影响用户体验与交互有效性。
- 适合关注AI公平性与跨语言交互的研究者阅读。
我们探讨大型语言模型（LLMs）在面对语言使用多样性的用户时，是否应调整或适配其语言行为。用户多样性可能源于年龄差异、性别特征以及多语经验，这些因素会带来语言处理与使用方式的差异。本文分析了这种多样性对可用性、沟通效率及大模型开发的潜在影响，强调在设计中考虑语言多样性的重要性。
原文摘要 · Abstract (English)
We discuss how desirable it is that Large Language Models (LLMs) be able to adapt or align their language behavior with users who may be diverse in their language use. User diversity may come about among others due to i) age differences; ii) gender characteristics, and/or iii) multilingual experience, and associated differences in language processing and use. We consider potential consequences for usability, communication, and LLM development.
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