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

This paper presents the first few-shot LLM-based chatbot that almost neverhallucinates and has high conversationality and low latency. WikiChat isgrounded on the English Wikipedia, the largest curated free-text corpus.  WikiChat generates a response from an LLM, retains only the grounded facts,and combines them with additional information it retrieves from the corpus toform factual and engaging responses. We distill WikiChat based on GPT-4 into a7B-parameter LLaMA model with minimal loss of quality, to significantly improveits latency, cost and privacy, and facilitate research and deployment.  Using a novel hybrid human-and-LLM evaluation methodology, we show that ourbest system achieves 97.3% factual accuracy in simulated conversations. Itsignificantly outperforms all retrieval-based and LLM-based baselines, and by3.9%, 38.6% and 51.0% on head, tail and recent knowledge compared to GPT-4.Compared to previous state-of-the-art retrieval-based chatbots, WikiChat isalso significantly more informative and engaging, just like an LLM.  WikiChat achieves 97.9% factual accuracy in conversations with human usersabout recent topics, 55.0% better than GPT-4, while receiving significantlyhigher user ratings and more favorable comments.

WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia