Related papers: Plurals: A System for Guiding LLMs Via Simulated Social Ensembles

Plurals: A System for Guiding LLMs Via Simulated Social Ensembles

URL: http://arxiv.org/abs/2409.17213v4
Date: Fri, 1 Nov 2024 02:08:03 GMT
Title: Plurals: A System for Guiding LLMs Via Simulated Social Ensembles
Authors: Joshua Ashkinaze, Emily Fry, Narendra Edara, Eric Gilbert, Ceren Budak,
Abstract summary: We introduce Plurals, a system and Python library for pluralistic AI deliberation. plurals consists of Agents which deliberate within customizable Structures, with Moderators overseeing deliberation. Six case studies demonstrate fidelity to theoretical constructs and efficacy.
Score: 1.9034114150823245
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Recent debates raised concerns that language models may favor certain viewpoints. But what if the solution is not to aim for a 'view from nowhere' but rather to leverage different viewpoints? We introduce Plurals, a system and Python library for pluralistic AI deliberation. Plurals consists of Agents (LLMs, optionally with personas) which deliberate within customizable Structures, with Moderators overseeing deliberation. Plurals is a generator of simulated social ensembles. Plurals integrates with government datasets to create nationally representative personas, includes deliberation templates inspired by democratic deliberation theory, and allows users to customize both information-sharing structures and deliberation behavior within Structures. Six case studies demonstrate fidelity to theoretical constructs and efficacy. Three randomized experiments show simulated focus groups produced output resonant with an online sample of the relevant audiences (chosen over zero-shot generation in 75% of trials). Plurals is both a paradigm and a concrete system for pluralistic AI. The Plurals library is available at https://github.com/josh-ashkinaze/plurals and will be continually updated.

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