Ensemble Debates with Local Large Language Models for AI Alignment
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914159607676928 |
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| author | Sarabamoun, Ephraiem |
| author_facet | Sarabamoun, Ephraiem |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_00091 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ensemble Debates with Local Large Language Models for AI Alignment Sarabamoun, Ephraiem Artificial Intelligence Computation and Language 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. |
| title | Ensemble Debates with Local Large Language Models for AI Alignment |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2509.00091 |