English(EN) STEER: Reducing Inference Cost in Relational Foundation Models through Semantically Informed Sampling

STEER采样方法将RFM推理成本降低40%

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-03 04:00

研究人员开发了STEER，一种旨在降低关系基础模型（RFM）推理成本的新型采样方法。RFM通常从数据库中采样相关行来形成其推理上下文，但这可能计算成本高昂。STEER通过使用大型语言模型来识别和优先处理给定预测任务最相关的表来解决这个问题。这种语义感知采样方法可以将推理上下文大小减少约40%，同时保持甚至提高预测准确性，这在三个最先进的RFM上得到了证明。 AI

影响降低关系基础模型的推理成本，可能促进更广泛的应用和更有效的部署。

排序理由详细介绍优化基础模型新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Abdalla Mohamed, Ashraf Aboulnaga · 2026-10-03 04:00

STEER：通过语义感知采样降低关系基础模型的推理成本

arXiv:2610.00907v1 Announce Type: cross Abstract: Relational foundation models (RFMs) are pretrained once on a collection of relational databases and prediction tasks, and then applied zero-shot to previously unseen databases and tasks. To make a prediction for a target row, an R…

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STEER：通过语义感知采样降低关系基础模型的推理成本

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