most citedDomain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Limited Linguistic Diversity in Embodied AI Datasets

Selma Wanna, Agnes Luhtaru, Jonathan Salfity +4

Language plays a critical role in Vision-Language-Action (VLA) models, yet the linguistic characteristics of the datasets used to train and evaluate these systems remain poorly doc…

cs.CL2024★ 1 cited

Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization

Ryan C. Barron, Ves Grantcharov, Selma Wanna +8

Large Language Models (LLMs) are pre-trained on large-scale corpora and excel in numerous general natural language processing (NLP) tasks, such as question answering (QA). Despite…

cs.LG2024

TopicTag: Automatic Annotation of NMF Topic Models Using Chain of Thought and Prompt Tuning with LLMs

Selma Wanna, Ryan Barron, Nick Solovyev +4

Topic modeling is a technique for organizing and extracting themes from large collections of unstructured text. Non-negative matrix factorization (NMF) is a common unsupervised app…

cs.AI2024

Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

Ryan Barron, Maksim E. Eren, Manish Bhattarai +6

Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document o…

cs.CR2024

Catch'em all: Classification of Rare, Prominent, and Novel Malware Families

Maksim E. Eren, Ryan Barron, Manish Bhattarai +5

National security is threatened by malware, which remains one of the most dangerous and costly cyber threats. As of last year, researchers reported 1.3 billion known malware specim…