mlsanityData to 14 Oct 2026

Ensemble Debates with Local Large Language Models for AI Alignment

arXiv 2509.00091 ↗First posted 27 Aug 2025cs.AI cs.CLcited by 0

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

Abstract (arXiv)

As large language models (LLMs) take on greater roles in high-stakes decisions, alignment with human values is essential. Reliance on proprietary APIs limits reproducibility and broad participation. We study whether local open-source ensemble debates can improve alignmentoriented reasoning. Across 150 debates spanning 15 scenarios and five ensemble configurations, ensembles outperform single-model baselines on a 7-point rubric (overall: 3.48 vs. 3.13), with the largest gains in reasoning depth (+19.4%) and argument quality (+34.1%). Improvements are strongest for truthfulness (+1.25 points) and human enhancement (+0.80). We provide code, prompts, and a debate data set, providing an accessible and reproducible foundation for ensemble-based alignment evaluation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 18 of its 22 references that are in the map. First posted on arXiv 27 Aug 2025.

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18 of 22 references.

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  1. 2022Training language models to follow instructions with human feedback4 Mar
  2. 2021A General Language Assistant as a Laboratory for Alignment1 Dec

Other references · 16

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  4. 2023Mistral 7Bcited by 3,164
  5. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
  6. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,917
  7. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
  8. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 1,011
  9. 2023Teaching Large Language Models to Self-Debugcited by 988
  10. 2022Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learnedcited by 729
  11. 2020Artificial Intelligence, Values and Alignmentcited by 448
  12. 2018Scalable agent alignment via reward modeling: a research directioncited by 447
4 more
  1. 2018AI safety via debatecited by 400
  2. 2021Unsolved Problems in ML Safetycited by 292
  3. 2021Alignment of Language Agentscited by 168
  4. 2023LM vs LM: Detecting Factual Errors via Cross Examinationcited by 156

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  2. 2023Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate
  3. 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignment
  4. 2023Large Language Model Alignment: A Survey
  5. 2025Dynamic Alignment for Collective Agency: Toward a Scalable Self-Improving Framework for Open-Ended LLM Alignment
  6. 2024Explanation, Debate, Align: A Weak-to-Strong Framework for Language Model Generalization
  7. 2025Decoding Alignment: A Critical Survey of LLM Development Initiatives through Value-setting and Data-centric Lens
  8. 2023Peering Through Preferences: Unraveling Feedback Acquisition for Aligning Large Language Models