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

Many commonsense reasoning NLP tasks involve choosing between one or morepossible answers to a question or prompt based on knowledge that is oftenimplicit. Large pretrained language models (PLMs) can achieve near-humanperformance on such tasks, while providing little human-interpretable evidenceof the underlying reasoning they use. In this work, we show how to use thesesame models to generate such evidence: inspired by the contrastive nature ofhuman explanations, we use PLMs to complete explanation prompts which contrastalternatives according to the key attribute(s) required to justify the correctanswer (for example, peanuts are usually salty while raisins are sweet).Conditioning model decisions on these explanations improves performance on twocommonsense reasoning benchmarks, as compared to previous non-contrastivealternatives. These explanations are also judged by humans to be more relevantfor solving the task, and facilitate a novel method to evaluate explanationfaithfulfness.

Prompting Contrastive Explanations for Commonsense Reasoning Tasks