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 demonstrated remarkable proficiency ingenerating fluent text. However, they often encounter the challenge ofgenerating inaccurate or hallucinated content. This issue is common in bothnon-retrieval-based generation and retrieval-augmented generation approaches,and existing post-hoc rectification methods may not address the accumulatedhallucination errors that may be caused by the "snowballing" issue, especiallyin reasoning tasks. To tackle these challenges, we introduce a novel approachcalled Real-time Verification and Rectification (Ever). Instead of waitinguntil the end of the generation process to rectify hallucinations, Ever employsa real-time, step-wise generation and hallucination rectification strategy. Theprimary objective is to detect and rectify hallucinations as they occur duringthe text generation process. When compared to both retrieval-based andnon-retrieval-based baselines, Ever demonstrates a significant improvement ingenerating trustworthy and factually accurate text across a diverse range oftasks, including short-form QA, biography generation, and multi-hop reasoning.

Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectification