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Found 3 papers, 1 papers with code
AutoRE: Document-Level Relation Extraction with Large Language Models
Large Language Models (LLMs) have demonstrated exceptional abilities in comprehending and generating text, motivating numerous researchers to utilize them for Information Extraction (IE) purposes, including Relation Extraction (RE).
Multiple User Context Inference by Fusing Data Sources
However, prevalent existing studies on user context inference have two shortcommings: 1. focusing on only a single data source (e. g. Internet browsing logs, or mobile call records), and 2. ignoring the interdependence of multiple user contexts (e. g. interdependence between age and marital status), which have led to poor inference performance.
On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient
Likelihood ratio policy gradient methods have been some of the most successful reinforcement learning algorithms, especially for learning on physical systems.
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