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

In conversational AI research, there's a noticeable trend towards developingmodels with a larger number of parameters, exemplified by models like ChatGPT.While these expansive models tend to generate increasingly better chatresponses, they demand significant computational resources and memory. Thisstudy explores a pertinent question: Can a combination of smaller modelscollaboratively achieve comparable or enhanced performance relative to asingular large model? We introduce an approach termed "blending", astraightforward yet effective method of integrating multiple chat AIs. Ourempirical evidence suggests that when specific smaller models aresynergistically blended, they can potentially outperform or match thecapabilities of much larger counterparts. For instance, integrating just threemodels of moderate size (6B/13B paramaeters) can rival or even surpass theperformance metrics of a substantially larger model like ChatGPT (175B+paramaters). This hypothesis is rigorously tested using A/B testingmethodologies with a large user base on the Chai research platform over a spanof thirty days. The findings underscore the potential of the "blending"strategy as a viable approach for enhancing chat AI efficacy without acorresponding surge in computational demands.

Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM