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 the potential to support a wide range ofapplications like conversational agents, creative writing, text improvement,and general query answering. However, they are ill-suited for query answeringin high-stake domains like medicine because they generate answers at random andtheir answers are typically not robust - even the same query can result indifferent answers when prompted multiple times. In order to improve therobustness of LLM queries, we propose using ranking queries repeatedly and toaggregate the queries using methods from social choice theory. We study rankingqueries in diagnostic settings like medical and fault diagnosis and discuss howthe Partial Borda Choice function from the literature can be applied to mergemultiple query results. We discuss some additional interesting properties inour setting and evaluate the robustness of our approach empirically.

Robust Knowledge Extraction from Large Language Models using Social Choice Theory