Presents REMOD, a graph-based supervised method for extracting semantic relations between entities in text to support modeling of online discourse and potential misinformation.
Agarwal, Chengkai Li, Jun Yang, and Cong Yu
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DFLOP is a data-driven framework that profiles data-induced computation variance and uses predictive scheduling to balance workloads in multimodal LLM training pipelines, claiming up to 3.6x faster training than existing frameworks.
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REMOD: Relation Extraction for Modeling Online Discourse
Presents REMOD, a graph-based supervised method for extracting semantic relations between entities in text to support modeling of online discourse and potential misinformation.
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DFLOP: A Data-driven Framework for Multimodal LLM Training Pipeline Optimization
DFLOP is a data-driven framework that profiles data-induced computation variance and uses predictive scheduling to balance workloads in multimodal LLM training pipelines, claiming up to 3.6x faster training than existing frameworks.