When Does Delegation Beat Majority? A Delegation-Based Aggregator for Multi-Sample LLM Inference
arXiv:2606.08098v1 Announce Type: cross Abstract: Majority voting over sampled answers is the dominant unsupervised aggregator for multi-sample LLM inference. We show that piping the signals every sample carries into a delegation-based aggregator (Propagational Proxy Voting, PPV) yields an unsupervised consensus rule that beats majority on MMLU-Pro by +1.5 pp overall and +2.24 pp on the non-trivial subset (paired McNemar p ~ 1.0e-14, n = 8,099). Majority discards two free signals every sample carries: within-group letter entropy and between-group reasoning geometry. PPV exposes two per-voter l
Ongoing research into optimizing LLM performance and efficiency continues to yield novel aggregation techniques to improve reliability.
This development offers a significant improvement in LLM inference accuracy without requiring additional data or model retraining, enhancing the utility of current and future LLMs.
The adoption of delegation-based aggregators like PPV could become a new standard for multi-sample LLM inference, making LLMs more robust and reliable for critical applications.
- · AI developers
- · LLM-powered application providers
- · Enterprise AI users
- · Legacy AI inference optimization methods
Improved reliability and accuracy of LLMs, especially in tasks requiring high precision.
Accelerated adoption of LLMs in fields where error tolerance is low, such as scientific research or complex decision-making systems.
This could contribute to the development of more sophisticated AI agents capable of higher-stake autonomous operations.
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Read at arXiv cs.LG