对比人类与大模型在日常任务中的隐含价值观差异
Implicit Values Embedded in How Humans and LLMs Complete Subjective Everyday Tasks
- 用30个日常任务审计6个主流大模型的隐含价值
- 大模型间及与人类在价值观上常不一致
- 揭示大模型决策背后的潜在偏见，适合关注AI伦理者
大型语言模型（LLMs）可支持智能助手完成日常任务，如推荐或基础计算。尽管前景广阔，但人们对这些助手在完成主观任务时展现的隐含价值知之甚少。人类可能重视环保、慈善、多样性等价值。大模型在完成日常任务时是否体现这些价值？与人类相比如何？我们通过审计六款主流大模型完成30个日常任务的表现，将其与100名美国众包工作者进行比较。结果发现，大模型在隐含价值表现上常与人类不一致，彼此之间也缺乏一致性。
原文摘要 · Abstract (English)
Large language models (LLMs) can underpin AI assistants that help users with everyday tasks, such as by making recommendations or performing basic computation. Despite AI assistants' promise, little is known about the implicit values these assistants display while completing subjective everyday tasks. Humans may consider values like environmentalism, charity, and diversity. To what extent do LLMs exhibit these values in completing everyday tasks? How do they compare with humans? We answer these questions by auditing how six popular LLMs complete 30 everyday tasks, comparing LLMs to each other and to 100 human crowdworkers from the US. We find LLMs often do not align with humans, nor with other LLMs, in the implicit values exhibited.
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