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Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration

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arxiv 2406.15951 v2 pith:NE4ZSIX7 submitted 2024-06-22 cs.CL

Shangbin Feng , Taylor Sorensen , Yuhan Liu , Jillian Fisher , Chan Young Park , Yejin Choi , Yulia Tsvetkov This is my paper · ORCID
classification cs.CL
keywords modularpluralismllmscommunityalignmentcommunitiesacrossadding
verification ladder T0 review T1 audit T2 compute T3 formal
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While existing alignment paradigms have been integral in developing large language models (LLMs), LLMs often learn an averaged human preference and struggle to model diverse preferences across cultures, demographics, and communities. We propose Modular Pluralism, a modular framework based on multi-LLM collaboration for pluralistic alignment: it "plugs into" a base LLM a pool of smaller but specialized community LMs, where models collaborate in distinct modes to flexibility support three modes of pluralism: Overton, steerable, and distributional. Modular Pluralism is uniquely compatible with black-box LLMs and offers the modular control of adding new community LMs for previously underrepresented communities. We evaluate Modular Pluralism with six tasks and four datasets featuring questions/instructions with value-laden and perspective-informed responses. Extensive experiments demonstrate that Modular Pluralism advances the three pluralism objectives across six black-box and open-source LLMs. Further analysis reveals that LLMs are generally faithful to the inputs from smaller community LLMs, allowing seamless patching by adding a new community LM to better cover previously underrepresented communities.

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Forward citations

Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 3,736 citations worldwide. Full citation record

  1. PLURAL: A Global Dataset for Value Alignment

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Synthetic preference data generated from the Integrated Values Survey preserves cross-country value differences and enables DPO fine-tuning that improves LLM cultural alignment across five countries.

  2. Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

    cs.CL 2026-04 unverdicted novelty 6.0 of 10

    Personalized RewardBench reveals that state-of-the-art reward models reach only 75.94% accuracy on personalized preferences and shows stronger correlation with downstream BoN and PPO performance than prior benchmarks.

  3. The Pluralistic Moral Gap: Understanding Judgment and Value Differences between Humans and Large Language Models

    cs.CL 2025-07 conditional novelty 6.0 of 10

    LLMs align with human moral judgments only under high consensus, concentrate on a narrow set of moral values, and the profile-based prompting method's reported improvement is evaluated in-sample.

  4. Epistemic diversity across language models mitigates knowledge collapse

    cs.LG 2025-12 reject novelty 5.0 of 10

    In repeated self-training loops on Wikitext2, ecosystems of four small language models show lower average perplexity than one, two, or sixteen models, but the paper's broader claims about monotonic optima, robustness,...

  5. How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs

    cs.MA 2025-07 conditional novelty 5.0 of 10

    A leader LLM trained with a GRPO variant that conditions on frozen agent responses improves both collaborative and zero-shot accuracy on BBH, MATH, and MMLU.

  6. Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration

    cs.NI 2025-07 conditional novelty 4.0 of 10

    A survey of multi-LLM systems in edge computing, covering architectures, enabling technologies, trust mechanisms, applications, and open datasets for edge general intelligence.

  7. Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead

    cs.SE 2025-06 conditional novelty 4.0 of 10

    A literature review organizes LLM development into a six-phase software engineering lifecycle and identifies challenges and research directions for each phase.

  8. Whispers of Many Shores: Cultural Alignment through Collaborative Cultural Expertise

    cs.AI 2025-05 reject novelty 4.0 of 10

    A multi-agent router that selects culturally specialized LLM personas reports a jump in self-scored cultural alignment from 0.208 to 0.820, but the metric and the claimed method are not independently validated.

  9. OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models

    cs.CY 2025-05 conditional novelty 4.0 of 10

    The paper advocates protecting and leveraging OpenReview's peer review corpus as a community asset for LLM-based review assistance, benchmarks, and alignment.

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