Beyond Majority Voting: LLM Aggregation by Leveraging Higher-Order Information

• URL: http://arxiv.org/abs/2510.01499v1
• Date: Wed, 01 Oct 2025 22:21:50 GMT
• Title: Beyond Majority Voting: LLM Aggregation by Leveraging Higher-Order Information
• Authors: Rui Ai, Yuqi Pan, David Simchi-Levi, Milind Tambe, Haifeng Xu,
• Abstract summary: We develop two new aggregation algorithms called Optimal Weight (OW) and Inverse Surprising Popularity (ISP)<n>Our theoretical analysis shows these methods provably mitigate inherent limitations of majority voting under mild assumptions.<n>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.
• Score: 57.397381631496906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 Optimal Weight (OW) and Inverse Surprising Popularity (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.

Related papers

• Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process [58.265053900416895]
  LLM-PeerReview is built on a novel, peer-review-inspired framework.<n>It operates in three stages: For scoring, we use the emerging LLM-as-a-Judge technique.<n>For reasoning, we can apply a graphical model-based truth inference algorithm.<n>Finally, the highest-scoring response is selected as the best ensemble output.
  arXiv  Detail & Related papers  (2025-12-29T05:25:49Z)
• Reasoning with Confidence: Efficient Verification of LLM Reasoning Steps via Uncertainty Heads [104.9566359759396]
  We propose a lightweight alternative for step-level reasoning verification based on data-driven uncertainty scores.<n>Our findings suggest that the internal states of LLMs encode their uncertainty and can serve as reliable signals for reasoning verification.
  arXiv  Detail & Related papers  (2025-11-09T03:38:29Z)
• Multi-Agent Evolve: LLM Self-Improve through Co-evolution [53.00458074754831]
  Reinforcement Learning (RL) has demonstrated significant potential in enhancing the reasoning capabilities of large language models (LLMs)<n>Recent Self-Play RL methods, inspired by the success of the paradigm in games and Go, aim to enhance LLM reasoning capabilities without human-annotated data.<n>We propose Multi-Agent Evolve (MAE), a framework that enables LLMs to self-evolve in solving diverse tasks, including mathematics, reasoning, and general knowledge Q&A.
  arXiv  Detail & Related papers  (2025-10-27T17:58:02Z)
• Uncertainty-Aware Answer Selection for Improved Reasoning in Multi-LLM Systems [55.6590601898194]
  Large Language Models (LLMs) have demonstrated exceptional capabilities, yet selecting the most reliable response from multiple LLMs remains a challenge.<n>Existing approaches often depend on costly external verifiers, human evaluators, or self-consistency techniques that require multiple samples from a single model.<n>We propose a principled, novel and computationally efficient method to select the best response from multiple different LLMs using a calibrated log-likelihood score.
  arXiv  Detail & Related papers  (2025-09-30T01:25:19Z)
• Learning to Refine: Self-Refinement of Parallel Reasoning in LLMs [102.48588475875749]
  We introduce Generative Self-Refinement (GSR), a novel parallel test-time scaling framework.<n>GSR generates a set of candidate responses in parallel and then performs self-refinement to synthesize a new superior solution.<n>We show that our method achieves state-of-the-art performance across five mathematical benchmarks.
  arXiv  Detail & Related papers  (2025-08-27T06:51:48Z)
• LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning [56.273799410256075]
  The framework combines Monte Carlo Tree Search (MCTS) with iterative Self-Refine to optimize the reasoning path. The framework has been tested on general and advanced benchmarks, showing superior performance in terms of search efficiency and problem-solving capability.
  arXiv  Detail & Related papers  (2024-10-03T18:12:29Z)
• SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language Models [8.558834738072363]
  Large language models (LLMs) have been widely adopted due to their remarkable performance across various applications.<n>These individual LLMs show limitations in generalization and performance on complex tasks due to inherent training biases, model size constraints, and the quality or diversity of pre-training datasets.<n>We introduce SelectLLM, which efficiently directs input queries to the most suitable subset of LLMs from a large pool.
  arXiv  Detail & Related papers  (2024-08-16T06:11:21Z)
• LAMPO: Large Language Models as Preference Machines for Few-shot Ordinal Classification [34.9210323553677]
  We introduce LAMPO, a novel paradigm that leverages Large Language Models (LLMs) for solving few-shot multi-class ordinal classification tasks. Extensive experiments on seven public datasets demonstrate LAMPO's remarkably competitive performance across a diverse spectrum of applications.
  arXiv  Detail & Related papers  (2024-08-06T15:55:05Z)
• Automated Multi-level Preference for MLLMs [41.72392895643214]
  Current multimodal Large Language Models (MLLMs) suffer from hallucination'' One promising path is to utilize reinforcement learning from human feedback (RLHF), which steers MLLMs towards learning superior responses while avoiding inferior ones. We rethink the common practice of using binary preferences (i.e., superior, inferior), and find that adopting multi-level preferences is better for two benefits.
  arXiv  Detail & Related papers  (2024-05-18T03:49:37Z)
• Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems [76.69936664916061]
  We study how the number of LM calls affects the performance of Vote and Filter-Vote. We find, surprisingly, that across multiple language tasks, the performance of both Vote and Filter-Vote can first increase but then decrease as a function of the number of LM calls.
  arXiv  Detail & Related papers  (2024-03-04T19:12:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.