The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models (2404.16019v2)

Abstract: Human feedback is central to the alignment of LLMs. However, open questions remain about methods (how), domains (where), people (who) and objectives (to what end) of feedback processes. To navigate these questions, we introduce PRISM, a dataset that 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. With PRISM, we contribute (i) wider geographic and demographic participation in feedback; (ii) census-representative samples for two countries (UK, US); and (iii) individualised ratings that link to detailed participant profiles, permitting personalisation and attribution of sample artefacts. We target subjective and multicultural perspectives on value-laden and controversial issues, where we expect interpersonal and cross-cultural disagreement. We use PRISM in three case studies to demonstrate the need for careful consideration of which humans provide what alignment data.

• The paper presents a novel Prism dataset capturing subjective human feedback on LLM alignment from 1,500 participants across 75 countries.
• The paper employs a participatory design with over 8,000 conversations involving 21 LLMs to ensure robust demographic and cultural representation.
• The paper demonstrates that analyzing individual and multicultural perspectives can guide personalized model alignment and ethical AI practices.

Exploring the Alignment of LLMs through Prism: A Dataset of Global Human Feedback

Introduction

The alignment of LLMs with human values and preferences remains a pressing concern in AI research, especially as these models become increasingly ubiquitous in various applications. The Prism dataset introduces a novel approach to understanding LLM alignment through detailed human feedback from a diverse, global cohort. This study aims to bridge significant gaps in current datasets by incorporating wide demographic and geocultural diversity, thereby enriching our understanding of human-model interactions across different societal norms and values.

Dataset Overview

Prism is designed with several innovative features aimed at enhancing the quality and applicability of the human feedback collected:

• Participatory Design: The dataset is generated via participatory mechanisms that involve 1,500 participants from 75 birth countries, engaging in over 8,000 conversations with 21 distinct LLMs. This design ensures a broad representation across different demographics and geographies, making the dataset robust for studying global perspectives.
• Representativeness: While acknowledging the challenges in achieving statistical representativeness, the dataset includes efforts to mirror national demographics in the UK and US, offering insights into collective preferences in these regions.
• Individualization: Each data point links back to an anonymized but detailed participant profile, allowing researchers to study individual preference nuances and detect potential biases or sample artefacts.
• Subjective and Multicultural Focus: The dataset emphasizes subjective opinions on value-laden topics, capturing a wide range of human beliefs and cultural perceptions that are often underrepresented in typical datasets.

Usage of the Dataset

Through the Prism dataset, researchers can explore various aspects of LLM alignment:

• Dialogue Diversity: Analysis of the dataset reveals variations in the topics discussed by different demographics, providing insights into the diverse ways individuals engage with LLMs.
• Preference Diversity: The dataset allows for the examination of how different groups may prefer various LLM alignments, highlighting the subjective nature of model preferences.
• Welfare Outcomes: Prism facilitates exploration into the broader implications of whose preferences are prioritized in LLM training, a crucial aspect for ethical AI development.

Potential for Future Research

The detailed nature of the Prism dataset opens several avenues for future research:

• Personalized Model Alignment: Researchers can utilize the individualized feedback and detailed demographic data to develop personalized alignment techniques, potentially improving user satisfaction and model effectiveness.
• Cross-cultural Model Evaluation: The multicultural aspect of the dataset provides a unique resource for studying how LLMs perform across different cultural contexts, which is vital for the development of globally fair and effective AI systems.
• Policy and Ethical Implications: By analyzing how different demographic groups react to and influence LLM behaviors, the dataset can inform policymakers and stakeholders about the societal impacts of AI, guiding more responsible AI development practices.

Conclusions

Prism represents a significant step forward in understanding and enhancing LLM alignment through human feedback. By incorporating a wide range of human perspectives, the dataset not only enriches the field of AI research but also addresses crucial ethical considerations in AI development. The findings from Prism underscore the importance of inclusive and diverse participatory processes in AI, ensuring that LLMs align well with the broad spectrum of human values and cultural contexts found globally.