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。

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

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