大型语言模型之所以存在偏见，是因为它们是大型语言模型
Large Language Models are Biased Because They Are Large Language Models
AI总结：
本文提出立场：按当前方式构建的LLM必然产生有害偏见，因此需重新审视LLM驱动AI的基础假设。
摘要：
本立场论文的主要目标是引发关于偏见与大型语言模型基本属性之间关系的深入讨论。为此，作者试图说服读者：按照目前LLM的构建方式，有害偏见是任何大型语言模型设计所产生的必然结果。如果这一观点成立，则意味着若不认真反思由LLM驱动的AI、不回溯其设计背后的基础假设，有害偏见问题就无法得到妥善解决。
英文摘要：
This position paper's primary goal is to provoke thoughtful discussion about the relationship between bias and fundamental properties of large language models. I do this by seeking to convince the reader that harmful biases are an inevitable consequence arising from the design of any large language model as LLMs are currently formulated. To the extent that this is true, it suggests that the problem of harmful bias cannot be properly addressed without a serious reconsideration of AI driven by LLMs, going back to the foundational assumptions underlying their design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
