HIERARCHICAL AND PEER PRUNING STRATEGIES FOR GENERATIVE ARTIFICIAL INTELLIGENCE MODELS IN TELECOMMUNICATIONS NETWORKS
개요
출원인
INTERNATIONAL BUSINESS MACHINES CORPORATION
발명자
Dinesh C. Verma; Sagar Tayal
IPC 분류
CPC 분류
Provided are a method, system, and computer program product for hierarchical inference utilizing a large language model (LLM). Training is performed at a central location, of a helper model and a pruned model for each layer of a hierarchy, wherein the helper model is trained to classify a request as appropriate for the pruned model, and wherein the pruned model is generated from a reduction process of the LLM. A process distributes the helper model and pruned model to different levels of the hierarchy. The process directs, by utilizing the helper model at each level of the hierarchy, inference generation to the pruned model or to another model at a higher tier.
원문 (중국어)
Provided are a method, system, and computer program product for hierarchical inference utilizing a large language model (LLM). Training is performed at a central location, of a helper model and a pruned model for each layer of a hierarchy, wherein the helper model is trained to classify a request as appropriate for the pruned model, and wherein the pruned model is generated from a reduction process of the LLM. A process distributes the helper model and pruned model to different levels of the hierarchy. The process directs, by utilizing the helper model at each level of the hierarchy, inference generation to the pruned model or to another model at a higher tier.