Human feedback plays a central role in the alignment of Large Language Models
(LLMs). However, open questions remain about the methods (how), domains
(where), people (who) and objectives (to what end) of human feedback
collection. To navigate these questions, we introduce PRISM, a new dataset
which maps the sociodemographics and stated preferences of 1,500 diverse
participants from 75 countries, to their contextual preferences and
fine-grained feedback in 8,011 live conversations with 21 LLMs. PRISM
contributes (i) wide geographic and demographic participation in human feedback
data; (ii) two census-representative samples for understanding collective
welfare (UK and US); and (iii) individualised feedback where every rating is
linked to a detailed participant profile, thus permitting exploration of
personalisation and attribution of sample artefacts. We focus on collecting
conversations that centre subjective and multicultural perspectives on
value-laden and controversial topics, where we expect the most interpersonal
and cross-cultural disagreement. We demonstrate the usefulness of PRISM via
three case studies of dialogue diversity, preference diversity, and welfare
outcomes, showing that it matters which humans set alignment norms. As well as
offering a rich community resource, we advocate for broader participation in AI
development and a more inclusive approach to technology design.

PRISM 是一项以人为导向的研究，通过调查 1,500 个来自 75 个国家具有不同社会经济背景和偏好的参与者与 21 个 LLMs 的 8,011 个实时对话，探讨人类反馈收集的方法、领域、人员和目标，并通过对话多样性、偏好多样性和福利结果等案例研究证明了 PRISM 的有用性，提倡更广泛的参与 AI 开发和更包容的技术设计。

PRISM 对鲍尔语言模型的主观和多元文化对齐的参与式、代表性和个性化人类反馈

The PRISM Alignment Project: What Participatory, Representative and  Individualised Human Feedback Reveals About the Subjective and Multicultural  Alignment of Large Language Models