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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Large Language Models (LLMs) have made progress in various real-world tasks,which stimulates requirements for the evaluation of LLMs. Existing LLMevaluation methods are mainly supervised signal-based which depends on staticdatasets and cannot evaluate the ability of LLMs in dynamic real-worldscenarios where deep interaction widely exists. Other LLM evaluation methodsare human-based which are costly and time-consuming and are incapable oflarge-scale evaluation of LLMs. To address the issues above, we propose a novelDeep Interaction-based LLM-evaluation framework. In our proposed framework,LLMs' performances in real-world domains can be evaluated from their deepinteraction with other LLMs in elaborately designed evaluation tasks.Furthermore, our proposed framework is a general evaluation method that can beapplied to a host of real-world tasks such as machine translation and codegeneration. We demonstrate the effectiveness of our proposed method throughextensive experiments on four elaborately designed evaluation tasks.

Beyond Static Datasets: A Deep Interaction Approach to LLM Evaluation