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

Recent advancements in large language models (LLMs) underscore theirpotential for responding to medical inquiries. However, ensuring thatgenerative agents provide accurate and reliable answers remains an ongoingchallenge. In this context, multi-agent debate (MAD) has emerged as a prominentstrategy for enhancing the truthfulness of LLMs. In this work, we provide acomprehensive benchmark of MAD strategies for medical Q&A, along withopen-source implementations. This explores the effective utilization of variousstrategies including the trade-offs between cost, time, and accuracy. We buildupon these insights to provide a novel debate-prompting strategy based on agentagreement that outperforms previously published strategies on medical Q&Atasks.

Are we going MAD? Benchmarking Multi-Agent Debate between Language Models for Medical Q&A