English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Information Extraction (IE) stands as a cornerstone in natural languageprocessing, traditionally segmented into distinct sub-tasks. The advent ofLarge Language Models (LLMs) heralds a paradigm shift, suggesting thefeasibility of a singular model addressing multiple IE subtasks. In this vein,we introduce the General Information Extraction Large Language Model (GIELLM),which integrates text Classification, Sentiment Analysis, Named EntityRecognition, Relation Extraction, and Event Extraction using a uniforminput-output schema. This innovation marks the first instance of a modelsimultaneously handling such a diverse array of IE subtasks. Notably, theGIELLM leverages the Mutual Reinforcement Effect (MRE), enhancing performancein integrated tasks compared to their isolated counterparts. Our experimentsdemonstrate State-of-the-Art (SOTA) results in five out of six Japanese mixeddatasets, significantly surpassing GPT-3.5-Turbo. Further, an independentevaluation using the novel Text Classification Relation and EventExtraction(TCREE) dataset corroborates the synergistic advantages of MRE intext and word classification. This breakthrough paves the way for most IEsubtasks to be subsumed under a singular LLM framework. Specialized fine-tunetask-specific models are no longer needed.

GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effect