Title: Beyond Majority Voting: LLM Aggregation by Leveraging Higher-Order Information
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Multi-agent LLMs, Information Aggregation, Collective Decision Making

Abstract: With the rapid progress of multi-agent large language model (LLM) reasoning, how to effectively aggregate answers from multiple LLMs has emerged as a fundamental challenge. Standard majority voting treats all answers equally, failing to consider latent heterogeneity and correlation across models. In this work, we design two new aggregation algorithms called $\underline{\text{O}}$ptimal $\underline{\text{W}}$eight (OW) and $\underline{\text{I}}$nverse $\underline{\text{S}}$urprising $\underline{\text{P}}$opularity (ISP), leveraging both first-order and second-order information. Our theoretical analysis shows these methods provably mitigate inherent limitations of majority voting under mild assumptions, leading to more reliable collective decisions.
We empirically validate our algorithms on synthetic datasets, popular LLM fine-tuning benchmarks such as UltraFeedback and MMLU, and a real-world healthcare setting ARMMAN. Across all cases, our methods consistently outperform majority voting, offering both practical performance gains and conceptual insights for the design of robust multi-agent LLM pipelines.