arXiv:2607.04389v1 Announce Type: new Abstract: It is increasingly common to aggregate predictions from multiple LLMs, each with domain expertise or access to private tools and data, to improve collective prediction performance. In decentralized settings, aggregation weights need to be determined without access to models' private information and should remain robust to strategic reporting. We propose a family of advantage-aligned wagering mechanisms for LLM aggregation (WALLA), in which each model reports a prediction and a learned wager, and predictions are aggregated using wagers as weights.
The increasing complexity and specialization of large language models necessitate robust mechanisms for combining their outputs, especially in decentralized and privacy-preserving environments.
This mechanism offers a path to improve collective AI prediction performance while safeguarding proprietary model information and ensuring robustness against strategic reporting.
The method of aggregating predictions from multiple LLMs can become more efficient, secure, and resilient, moving beyond simple averaging or black-box ensemble methods.
- · LLM developers (specialized)
- · AI-powered decision-making platforms
- · Data privacy advocates
- · Decentralized AI applications
- · Centralized AI aggregation services
- · Unsophisticated LLM ensemble methods
Improved accuracy and reliability in AI prediction markets and multi-agent systems.
Reduced need for direct model access, potentially fostering more robust competition among specialized AI models without revealing their internal workings.
Emergence of new business models based on selling 'wagered' predictions from proprietary LLMs without exposing the underlying models themselves.
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Read at arXiv cs.AI