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Scalable Delphi uses LLMs to streamline risk estimation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Scalable Delphi, a novel approach that adapts the traditional Delphi method for large language models. This new method aims to make structured risk estimation more accessible and efficient by using LLMs with diverse expert personas, iterative refinement, and rationale sharing. Scalable Delphi reduces the time and cost associated with traditional methods, allowing for dynamic analysis of estimates and their underlying evidence across various domains, including cybersecurity and climate science. AI

IMPACT This research could make structured risk assessment more accessible and dynamic across various fields by leveraging LLMs.

RANK_REASON This is a research paper describing a new method for risk estimation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Scalable Delphi uses LLMs to streamline risk estimation

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This is a research paper describing a new method for risk estimation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Tobias Lorenz, Mario Fritz ·

    Scalable Delphi: Large Language Models for Structured Risk Estimation

    arXiv:2602.08889v2 Announce Type: replace Abstract: Quantitative risk assessment relies on structured expert elicitation to estimate unobservable properties. The Delphi method produces calibrated, auditable estimates but requires months of coordination and specialist time, placin…

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