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

Learning paradigms for large language models (LLMs) currently tend to fallwithin either in-context learning (ICL) or full fine-tuning. Each of thesecomes with their own trade-offs based on available data, model size, computecost, ease-of-use, and final quality with neither solution performing wellacross-the-board. In this article, we first describe ICL and fine-tuningparadigms in a way that highlights their natural connections. Based on theseconnections, we propose a new learning paradigm called FIAT that fuses the bestof these paradigms together, enabling prompt-engineered instructions andchain-of-thought reasoning with the very largest models while also usingsimilar methods to perform parameter updates on a modestly-sized LLM withparameter-efficient tuning. We evaluate FIAT's effectiveness on a variety ofmultilingual tasks and observe that FIAT performs better than both ICL andfine-tuning at scales ranging from 100-10,000 training examples. We hope thatFIAT provides a practical way of harnessing the full potential of LLMs withoutneeding to make a hard choice between learning paradigms.

FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning