Revealing Trends in Datasets from the 2022 ACL and EMNLP Conferences

no code implementations • 31 Mar 2024 • Jesse Atuhurra, Hidetaka Kamigaito

NLP systems are on par or, in some cases, better than humans at accomplishing specific tasks.

Dealing with Imbalanced Classes in Bot-IoT Dataset

no code implementations • 27 Mar 2024 • Jesse Atuhurra, Takanori Hara, Yuanyu Zhang, Masahiro Sasabe, Shoji Kasahara

To evaluate the robustness of the NIDS in the IoT network, the existing work proposed a realistic botnet dataset in the IoT network (Bot-IoT dataset) and applied it to machine learning-based anomaly detection.

Anomaly Detection Binary Classification +1

Distilling Named Entity Recognition Models for Endangered Species from Large Language Models

no code implementations • 13 Mar 2024 • Jesse Atuhurra, Seiveright Cargill Dujohn, Hidetaka Kamigaito, Hiroyuki Shindo, Taro Watanabe

Natural language processing (NLP) practitioners are leveraging large language models (LLM) to create structured datasets from semi-structured and unstructured data sources such as patents, papers, and theses, without having domain-specific knowledge.

In-Context Learning Knowledge Distillation +5