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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Generating step-by-step "chain-of-thought" rationales improves language modelperformance on complex reasoning tasks like mathematics or commonsensequestion-answering. However, inducing language model rationale generationcurrently requires either constructing massive rationale datasets orsacrificing accuracy by using only few-shot inference. We propose a techniqueto iteratively leverage a small number of rationale examples and a largedataset without rationales, to bootstrap the ability to perform successivelymore complex reasoning. This technique, the "Self-Taught Reasoner" (STaR),relies on a simple loop: generate rationales to answer many questions, promptedwith a few rationale examples; if the generated answers are wrong, try again togenerate a rationale given the correct answer; fine-tune on all the rationalesthat ultimately yielded correct answers; repeat. We show that STaRsignificantly improves performance on multiple datasets compared to a modelfine-tuned to directly predict final answers, and performs comparably tofine-tuning a 30$\times$ larger state-of-the-art language model onCommensenseQA. Thus, STaR lets a model improve itself by learning from its owngenerated reasoning.

STaR: Bootstrapping Reasoning With Reasoning