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 the past 20 years, artificial neural networks have become dominant invarious areas, continually growing in scale. However, the current analysis oflarge models has mainly focused on functionality, overlooking the influence ofscale differences on their properties. To address this, we propose the conceptof Emergence Learning, which emphasizes the significance of scale. By studyingmodels of different scales, we have identified a key factor in achieving higherperformance in large models: the decrease of monosemantic neurons. Building onthis insight, we propose a proactive approach to inhibit monosemanticity forimproved performance. Our solution involves a two-phase process that includesmonosemantic neuron detection and inhibition, supported by theoreticalanalysis. Experimental results on various tasks and neural networks demonstratethe effectiveness of our proposed method.  Following the idea of Emergence Learning, though drawing inspiration fromscaling phenomena, the applicability of our method is not restricted to largescale alone. Therefore, the experiment is self-contained. However, extendingthis research to very large-scale datasets is appealing yet impossible forresearch departments due to limited resources. We are delighted to share thefirst co-authorship and eagerly await collaboration from any AI company beforesubmission.

Emergence Learning: A Rising Direction from Emergent Abilities and a Monosemanticity-Based Study