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

While large language models (LLMs) are equipped with longer text inputcapabilities than before, they are struggling to seek correct information inlong contexts. The "lost in the middle" problem challenges most LLMs, referringto the dramatic decline in accuracy when correct information is located in themiddle. To overcome this crucial issue, this paper proposes to enhance theinformation searching and reflection ability of LLMs in long contexts viaspecially designed tasks called Attention Strengthening Multi-doc QA (ASM QA).Following these tasks, our model excels in focusing more precisely on thedesired information. Experimental results show substantial improvement inMulti-doc QA and other benchmarks, superior to state-of-the-art models by 13.7%absolute gain in shuffled settings, by 21.5% in passage retrieval task. Werelease our model, Ziya-Reader to promote related research in the community.

Never Lost in the Middle: Improving Large Language Models via Attention Strengthening Question Answering