Can LLMs simulate diverse viewpoints for better decisions?
Plurals: A System for Guiding LLMs Via Simulated Social Ensembles
September 27, 2024
https://arxiv.org/pdf/2409.17213This research introduces Plurals, a Python library for creating multi-agent AI systems where different large language models (LLMs) interact and deliberate with each other. It leverages the principles of democratic deliberation to guide LLM interaction and aims to produce outputs that are more nuanced and representative of diverse viewpoints than single-LLM approaches.
Plurals focuses on:
- Representing Diverse Perspectives: It allows integration with demographic datasets (e.g., ANES) to create LLMs with diverse personas and viewpoints.
- Structuring Information Flow: The library provides different structures (e.g., debates, chains, graphs) to control how information is shared between LLMs, enabling diverse deliberation strategies.
- Steering Deliberation: It provides tools to guide how individual LLMs combine information from others, allowing users to steer the deliberation process towards desired outcomes.
- Moderation and Summarization: It includes Moderators that can summarize and aggregate the output of multi-agent deliberations, enabling easier interpretation and analysis.