Large Models-Driven Deduction for Emergency Instances
Researchers have proposed the Large Models-driven World Line Divergence System (WLDS) to address limitations in traditional emergency simulation methods. Existing systems often lack randomness and diversity, struggling to explore potential risks due to scarce emergency instance data. In contrast, WLDS leverages Large Models (LMs) to dynamically adjust generation strategies, introducing controllable randomness while utilizing extensive prior knowledge for cross-domain transfer. The system deduces emergency instances across various development directions, employing factual and logical calibration mechanisms to ensure accuracy and rigor. An interactive module allows users to select deduction paths, mitigating potential hallucinations, while a visualization module combines text and images to enhance interpretability. Extensive experiments on the new Emergency Instances Deduction (EID) benchmark dataset demonstrate that WLDS achieves high-precision and high-fidelity simulations in multiple domains. This approach generates diverse deduction data, supporting better risk assessment and decision-making for future emergency scenarios by overcoming the static nature of preset simulations.
Wire timeline
Large Models-Driven Deduction for Emergency Instances
Researchers have proposed the Large Models-driven World Line Divergence System (WLDS) to address limitations in traditional emergency simulation methods. Existing systems often lack randomness and diversity, struggling to explore potential risks due to scarce emergency instance data. In contrast, WLDS leverages Large Models (LMs) to dynamically adjust generation strategies, introducing controllable randomness while utilizing extensive prior knowledge for cross-domain transfer. The system deduces emergency instances across various development directions, employing factual and logical calibration mechanisms to ensure accuracy and rigor. An interactive module allows users to select deduction paths, mitigating potential hallucinations, while a visualization module combines text and images to enhance interpretability. Extensive experiments on the new Emergency Instances Deduction (EID) benchmark dataset demonstrate that WLDS achieves high-precision and high-fidelity simulations in multiple domains. This approach generates diverse deduction data, supporting better risk assessment and decision-making for future emergency scenarios by overcoming the static nature of preset simulations.
cs.AI updates on arXiv.org