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 received significant attention by achievingremarkable performance across various tasks. However, their fixed contextlength poses challenges when processing long documents or maintaining extendedconversations. This paper proposes a method called \textit{Selective Context}that employs self-information to filter out less informative content, therebyenhancing the efficiency of the fixed context length. We demonstrate theeffectiveness of our approach on tasks of summarisation and question answeringacross different data sources, including academic papers, news articles, andconversation transcripts.

Unlocking Context Constraints of LLMs: Enhancing Context Efficiency of LLMs with Self-Information-Based Content Filtering