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

In adversarial data collection (ADC), a human workforce interacts with amodel in real time, attempting to produce examples that elicit incorrectpredictions. Researchers hope that models trained on these more challengingdatasets will rely less on superficial patterns, and thus be less brittle.However, despite ADC's intuitive appeal, it remains unclear when training onadversarial datasets produces more robust models. In this paper, we conduct alarge-scale controlled study focused on question answering, assigning workersat random to compose questions either (i) adversarially (with a model in theloop); or (ii) in the standard fashion (without a model). Across a variety ofmodels and datasets, we find that models trained on adversarial data usuallyperform better on other adversarial datasets but worse on a diverse collectionof out-of-domain evaluation sets. Finally, we provide a qualitative analysis ofadversarial (vs standard) data, identifying key differences and offeringguidance for future research.

On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study