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View article: Predictive Indicators of the Performance of Large Language Models
Predictive Indicators of the Performance of Large Language Models Open
In several mission contexts, it is desirable to estimate the performance of large language models (LLMs) on tasks that we cannot run directly. In light of published “scaling laws” our hypothesis is that some tasks should be consistently mo…
View article: A Summary of Advances in Document Summarization from 2023-2024
A Summary of Advances in Document Summarization from 2023-2024 Open
In computer science, Document Summarization is the task of condensing some quantity of text and related content through automated means. In this document, we review recent literature in text summarization. “Hybrid” extractive-abstractive a…
View article: Social Media Analytics Relevant to TikTok - a Literature Review and Directions for Future Research
Social Media Analytics Relevant to TikTok - a Literature Review and Directions for Future Research Open
We have attempted to capture a sense of the scientific state of the art in studying social media platforms, including data collection from platforms, understanding platform behavior, known adversarial uses, and adverse content detection, c…
View article: Identifying Disinformation Using Rhetorical Devices in Natural Language Models
Identifying Disinformation Using Rhetorical Devices in Natural Language Models Open
Foreign disinformation campaigns are strategically organized, extended efforts using disinformation – false or misleading information deliberately placed by an adversary – to achieve some goal. Disinformation campaigns pose severe threats …
View article: Survey of Current State of the Art Entity-Relation Extraction Tools
Survey of Current State of the Art Entity-Relation Extraction Tools Open
In the area of information extraction from text data, there exists a number of tools with the capability of extracting entities, topics, and their relationships with one another from both structured and unstructured text sources. Such info…
View article: Survey of Current State of the Art Entity-Relation Extraction Tools
Survey of Current State of the Art Entity-Relation Extraction Tools Open
In the realm of information extraction from text data, there exists a number of tools with the capability of extracting entities and their relationships with one another. Such information has endless uses in a number of domains, however, t…
View article: Adverse Event Prediction Using Graph-Augmented Temporal Analysis (Final Report)
Adverse Event Prediction Using Graph-Augmented Temporal Analysis (Final Report) Open
This report summarizes the work performed under the Sandia LDRD project "Adverse Event Prediction Using Graph-Augmented Temporal Analysis." The goal of the project was to develop a method for analyzing multiple time-series data streams to …
View article: Adverse Event Prediction Using Graph-Augmented Temporal Analysis (Final Report)
Adverse Event Prediction Using Graph-Augmented Temporal Analysis (Final Report) Open
This report summarizes the work performed under the Sandia LDRD project "Adverse Event Prediction Using Graph-Augmented Temporal Analysis." The goal of the project was to develop a method for analyzing multiple time-series data streams to …
View article: A dynamic model for social networks
A dynamic model for social networks Open
Social network graph models are data structures representing entities (often people, corporations, or accounts) as "vertices" and their interactions as "edges" between pairs of vertices. These graphs are most often total-graph models — the…
View article: Efficient Transfer Learning for Neural Network Language Models
Efficient Transfer Learning for Neural Network Language Models Open
We apply transfer learning techniques to create topically and/or stylistically biased natural language models from small data samples, given generic long short-term memory (LSTM) language models trained on larger data sets. Although LSTM l…
View article: Estimating users' mode transition functions and activity levels from social media
Estimating users' mode transition functions and activity levels from social media Open
We present a temporal model of individual-scale social media user behavior, comprising modal activity levels and mode switching patterns. We show that this model can be effectively and easily learned from available social media data, and t…