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 this paper, we explore a new way for user targeting, where non-expertmarketers could select their target users solely given demands in naturallanguage form. The key to this issue is how to transform natural languages intopractical structured logical languages, i.e., the structured understanding ofmarketer demands. Considering the impressive natural language processingability of large language models (LLMs), we try to leverage LLMs to solve thisissue. Past research indicates that the reasoning ability of LLMs can beeffectively enhanced through chain-of-thought (CoT) prompting. But existingmethods still have some limitations: (1) Previous methods either use simple"Let's think step by step" spells or provide fixed examples in demonstrationswithout considering compatibility between prompts and questions, making LLMsineffective in some complex reasoning tasks such as structured languagetransformation. (2) Previous methods are often implemented in closed-sourcemodels or excessively large models, which is not suitable in industrialpractical scenarios. Based on these, we propose ARALLM (i.e., AnalogicalReasoning Augmented Large Language Models) consisting of two modules:Analogical Reasoning based Prompting and Reasoning-Augmented Multi-Task ModelDistillation.

Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs