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

The recent development and success of Large Language Models (LLMs)necessitate an evaluation of their performance across diverse NLP tasks indifferent languages. Although several frameworks have been developed and madepublicly available, their customization capabilities for specific tasks anddatasets are often complex for different users. In this study, we introduce theLLMeBench framework. Initially developed to evaluate Arabic NLP tasks usingOpenAI's GPT and BLOOM models; it can be seamlessly customized for any NLP taskand model, regardless of language. The framework also features zero- andfew-shot learning settings. A new custom dataset can be added in less than 10minutes, and users can use their own model API keys to evaluate the task athand. The developed framework has been already tested on 31 unique NLP tasksusing 53 publicly available datasets within 90 experimental setups, involvingapproximately 296K data points. We plan to open-source the framework for thecommunity (https://github.com/qcri/LLMeBench/). A video demonstrating theframework is available online (https://youtu.be/FkQn4UjYA0s).

LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking